- published: 28 May 2009
- views: 114826
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Casey Whitelaw describes the natural language processing behind Google Wave's spelling correction on the deck of the Sydney office. Birds and boat horns for effect.
What do Bruce Springsteen and natural language processing have in common? Get the rundown from Expect Labs' Research Director, Marsal Gavaldà. http://www.expectlabs.com
TRANSCRIPT:
Very often we get asked the question, "what is NLP?" Let me try to give a quick answer. NLP, natural language processing, or actually NLU, natural language understanding, is the set of methodologies and techniques that allow for a computer to make sense of human language. Human language meaning English, Swahili, Chinese; whatever language you speak, as opposed to a computer language, a programming language. So it's the process of analyzing these words and then trying to extract the meaning. For example, if I'm interfacing with an MP3 player I can say, "can you play me a song by Bruce Springsteen where he complains about having to drive his girlfriend's mother?" Understanding this full sentence means that we have to understand the key part in this case is "play," and that play is a verb as opposed to another example where "I want to see a play," and play is a noun. The context is important, which is why it's difficult for computers to do this process, it's much easier for humans because as we learn, we interact with the world and we subconsciously develop a way to disambiguate and to not get confused by whether "play" is being used as a verb or as a noun.
So what is the song by Bruce Springsteen where he complains about having to drive his girlfriend's mom? Well, it turns out it's "Sherry Darling." The interesting part is that if you have access to all the lyrics, it's not super complicated to have a good indexing of those lyrics and then being able to answer and find the most relevant answers to questions like "which song is the one where something happens?" It's interesting because many times we remember the content of the song, and we don't necessarily remember the title of the song, so it's quite useful to be able to go deeper and index all the lyrics. This is something that is very doable using the MindMeld API, where you can just crawl a website. We internally create our custom knowledge graph and then make it very easy to have a voice-driven interface to this content, voice-driven search and discovery. So you could actually crawl a website with lyrics and then ask this question about this song by Bruce Springsteen where he has to drive his girlfriend's mother and you would say "play" and it would start playing.
- published: 19 Aug 2014
- views: 3897
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language.
The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text.
NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more!
Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK!
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com
- published: 01 May 2015
- views: 29936
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey simple literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself.
- published: 03 Aug 2015
- views: 272645
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
- published: 16 Oct 2008
- views: 41493
Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience.
Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising!
We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart.
In this talk, we plan to cover the following:
Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.
New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets - up from 85% by statistical models)
Share some of the learnings we have had while deploying these in Flipkart!
Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html
We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute!
- published: 18 Jul 2015
- views: 2841
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects).
- published: 03 Aug 2015
- views: 52849
In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson's Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with MetaMind.io's software.
Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 'Magic Grant' from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.
- published: 10 Jun 2015
- views: 5436
How can we describe the complexity of linguistic systems? Where does natural language fit in? In this week's episode, we talk about the Chomsky hierarchy: what it captures, what characterizes different kinds of grammars on the hierarchy, and whether we can find grammars that sit higher on the scale than human language.
This is Topic #63!
This week's tag language: Croatian!
Related topics:
Happy Little Trees: X' Theory - https://youtu.be/7UOcoQr0hvg
Trace Evidence: Syntactic Movement - https://youtu.be/x5iBbSkp8rk
Last episode:
Quantifying Sets and Toasters: Generalized Quantifiers - https://youtu.be/U1l3C_hmjqM
Other of our syntax and morphology videos:
Raising the Bar: Raising and Control Verbs - https://youtu.be/SYoYNeaSYrU
Organizing Meanings: Morphological Typology - https://youtu.be/Ts2DS0ZsTyo
Referential Treatment: Binding Theory - https://youtu.be/9sqm_cex4kA
Also, if you'd like to know more about the Chomsky Hierarchy and its impact on computer science, Computerphile's had a few videos about them:
- Their episode on the hierarchy: https://www.youtube.com/watch?v=224plb3bCog.
- Their episode about finite-state machines: https://www.youtube.com/watch?v=vhiiia1_hC4.
- And their episode about how finite-state machines relate to grammar: https://www.youtube.com/watch?v=RjOCRYdg8BY.
Find us on all the social media worlds:
Tumblr: http://thelingspace.tumblr.com/
Twitter: http://twitter.com/TheLingSpace
Facebook: http://www.facebook.com/thelingspace/
And at our website, http://www.thelingspace.com/ !
You can also find our store at the website, https://thelingspace.storenvy.com/
Our website also has extra content about this week's topic at http://www.thelingspace.com/episode-63/
(Or it will by Thursday morning.)
We also have forums to discuss this episode, and linguistics more generally.
Sources:
(1) I-Language (1st Edition, Daniela isac & Charles Reiss)
(2) Introduction to the Theory of Computation (3rd Edition, Michael Sipser)
(3) Mathematical Logic for Computer Science (3rd Edition, Mordechai Ben-Ari)
(4) Evidence Against the Context-Freeness of Natural Language (Stuart Shieber - http://www.eecs.harvard.edu/~shieber/Biblio/Papers/shieber85.pdf)
(5) https://en.wikipedia.org/wiki/Chomsky_hierarchy
(6) Syntactic Structures (Noam Chomsky)
(7) Mathematical Methods in Linguistics (Barbara Partee, Alice G. ter Meulen, Robert Wall)
Proof regarding crossing dependencies (adapted from the first edition of Introduction to Automata Theory, Languages, and Computation, by John Hopcroft and Jeffrey Ullman. Note where carets appear that the following character should be taken as superscript):
We first capture the general pattern of embedded clauses in Swiss German with the language a^nb^mc^nd^m . We then treat this as the result of intersecting Swiss German with the regular language a*b*c*d*.
Now, let L = {a^nb^mc^nd^m | n ≥ 1 and m ≥ 1}. Suppose L is a context-free language, and let p be the pumping length referred to in the pumping lemma for context-free languages (https://en.wikipedia.org/wiki/Pumping_lemma_for_context-free_languages). Consider the string z = a^pb^pc^pd^p. Let z = uvwxy satisfy the conditions of the pumping lemma. Then as |vwx| ≤ p, vx can contain at most two different symbols. Furthermore, if vx contains two different symbols, they must be consecutive, for example, a and b. If vx has only a’s, then uwy has fewer a’s than c’s and is not in L, a contradiction. We proceed similarly if vx consists of only b’s, only c’s, or only d’s. Now suppose that vx has a’s and b’s. Then uwy still has fewer a’s than c’s. A similar contradiction occurs if vx consist of b’s and c’s or c’s and d’s. Since these are the only possibilities, we conclude that L is not context-free.
Since context-free languages are closed under intersection with regular languages, and the above intersection is not context-free, Swiss German must be non-context-free.
Q.E.D.
A proof of the pumping lemma itself can be found in Introduction to the Theory of Computation (among other places). For a discussion of the closure properties of context-free languages, see Mathematical Methods in Linguistics (among other places).
Looking forward to next week!
- published: 02 Mar 2016
- views: 134
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal directives. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects). It also rejects directives that it is not obligated to do based on the relative social relationship between the robot and user.
- published: 03 Aug 2015
- views: 31201
In the philosophy of language, a natural language (or ordinary language) is any language which arises in an unpremeditated fashion as the result of the innate facility for language possessed by the human intellect. A natural language is typically used for communication, and may be spoken, signed, or written. Natural language is distinguished from constructed languages and formal languages such as computer-programming languages or the "languages" used in the study of formal logic, especially mathematical logic.
Though the exact definition varies between scholars, natural language can broadly be defined in contrast on the one hand to artificial or constructed languages, such as computer programming languages like Python and international auxiliary languages like Esperanto, and on the other hand to other communication systems in nature, such as the waggle dance of bees. Although there are a variety of natural languages, any cognitively normal human infant is able to learn any natural language. By comparing the different natural languages, scholars hope to learn something about the nature of human intelligence and the innate biases and constraints that shape natural language, which are sometimes called universal grammar.
This page contains text from Wikipedia, the Free Encyclopedia -
http://en.wikipedia.org/wiki/Natural_language
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
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5:05Google Wave: Natural Language Processing
Google Wave: Natural Language ProcessingGoogle Wave: Natural Language Processing
Casey Whitelaw describes the natural language processing behind Google Wave's spelling correction on the deck of the Sydney office. Birds and boat horns for effect. -
3:13NLP Fundamentals: What is Natural Language Processing?
NLP Fundamentals: What is Natural Language Processing?NLP Fundamentals: What is Natural Language Processing?
What do Bruce Springsteen and natural language processing have in common? Get the rundown from Expect Labs' Research Director, Marsal Gavaldà. http://www.expectlabs.com TRANSCRIPT: Very often we get asked the question, "what is NLP?" Let me try to give a quick answer. NLP, natural language processing, or actually NLU, natural language understanding, is the set of methodologies and techniques that allow for a computer to make sense of human language. Human language meaning English, Swahili, Chinese; whatever language you speak, as opposed to a computer language, a programming language. So it's the process of analyzing these words and then trying to extract the meaning. For example, if I'm interfacing with an MP3 player I can say, "can you play me a song by Bruce Springsteen where he complains about having to drive his girlfriend's mother?" Understanding this full sentence means that we have to understand the key part in this case is "play," and that play is a verb as opposed to another example where "I want to see a play," and play is a noun. The context is important, which is why it's difficult for computers to do this process, it's much easier for humans because as we learn, we interact with the world and we subconsciously develop a way to disambiguate and to not get confused by whether "play" is being used as a verb or as a noun. So what is the song by Bruce Springsteen where he complains about having to drive his girlfriend's mom? Well, it turns out it's "Sherry Darling." The interesting part is that if you have access to all the lyrics, it's not super complicated to have a good indexing of those lyrics and then being able to answer and find the most relevant answers to questions like "which song is the one where something happens?" It's interesting because many times we remember the content of the song, and we don't necessarily remember the title of the song, so it's quite useful to be able to go deeper and index all the lyrics. This is something that is very doable using the MindMeld API, where you can just crawl a website. We internally create our custom knowledge graph and then make it very easy to have a voice-driven interface to this content, voice-driven search and discovery. So you could actually crawl a website with lyrics and then ask this question about this song by Bruce Springsteen where he has to drive his girlfriend's mother and you would say "play" and it would start playing. -
19:54Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
Natural Language Processing With Python and NLTK p.1 Tokenizing words and SentencesNatural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com -
1:02Simple Natural Language Interaction with Consequence Reasoning
Simple Natural Language Interaction with Consequence ReasoningSimple Natural Language Interaction with Consequence Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey simple literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself. -
57:29Lecture - 39 Natural Language Processing - I
Lecture - 39 Natural Language Processing - ILecture - 39 Natural Language Processing - I
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in. -
19:02IBM Watson: Using the Natural Language Classifier in IBM Bluemix!
IBM Watson: Using the Natural Language Classifier in IBM Bluemix! -
44:43Devashish Shankar - Deep Learning for Natural Language Processing
Devashish Shankar - Deep Learning for Natural Language ProcessingDevashish Shankar - Deep Learning for Natural Language Processing
Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience. Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising! We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart. In this talk, we plan to cover the following: Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets - up from 85% by statistical models) Share some of the learnings we have had while deploying these in Flipkart! Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute! -
1:07Natural Language Interaction with ISA Handling and Consequence Reasoning
Natural Language Interaction with ISA Handling and Consequence ReasoningNatural Language Interaction with ISA Handling and Consequence Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects). -
45:14Text By the Bay 2015: Richard Socher, Deep Learning for Natural Language Processing
Text By the Bay 2015: Richard Socher, Deep Learning for Natural Language ProcessingText By the Bay 2015: Richard Socher, Deep Learning for Natural Language Processing
In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson's Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with MetaMind.io's software. Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 'Magic Grant' from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award. -
9:39How Complex is Natural Language? The Chomsky Hierarchy
How Complex is Natural Language? The Chomsky HierarchyHow Complex is Natural Language? The Chomsky Hierarchy
How can we describe the complexity of linguistic systems? Where does natural language fit in? In this week's episode, we talk about the Chomsky hierarchy: what it captures, what characterizes different kinds of grammars on the hierarchy, and whether we can find grammars that sit higher on the scale than human language. This is Topic #63! This week's tag language: Croatian! Related topics: Happy Little Trees: X' Theory - https://youtu.be/7UOcoQr0hvg Trace Evidence: Syntactic Movement - https://youtu.be/x5iBbSkp8rk Last episode: Quantifying Sets and Toasters: Generalized Quantifiers - https://youtu.be/U1l3C_hmjqM Other of our syntax and morphology videos: Raising the Bar: Raising and Control Verbs - https://youtu.be/SYoYNeaSYrU Organizing Meanings: Morphological Typology - https://youtu.be/Ts2DS0ZsTyo Referential Treatment: Binding Theory - https://youtu.be/9sqm_cex4kA Also, if you'd like to know more about the Chomsky Hierarchy and its impact on computer science, Computerphile's had a few videos about them: - Their episode on the hierarchy: https://www.youtube.com/watch?v=224plb3bCog. - Their episode about finite-state machines: https://www.youtube.com/watch?v=vhiiia1_hC4. - And their episode about how finite-state machines relate to grammar: https://www.youtube.com/watch?v=RjOCRYdg8BY. Find us on all the social media worlds: Tumblr: http://thelingspace.tumblr.com/ Twitter: http://twitter.com/TheLingSpace Facebook: http://www.facebook.com/thelingspace/ And at our website, http://www.thelingspace.com/ ! You can also find our store at the website, https://thelingspace.storenvy.com/ Our website also has extra content about this week's topic at http://www.thelingspace.com/episode-63/ (Or it will by Thursday morning.) We also have forums to discuss this episode, and linguistics more generally. Sources: (1) I-Language (1st Edition, Daniela isac & Charles Reiss) (2) Introduction to the Theory of Computation (3rd Edition, Michael Sipser) (3) Mathematical Logic for Computer Science (3rd Edition, Mordechai Ben-Ari) (4) Evidence Against the Context-Freeness of Natural Language (Stuart Shieber - http://www.eecs.harvard.edu/~shieber/Biblio/Papers/shieber85.pdf) (5) https://en.wikipedia.org/wiki/Chomsky_hierarchy (6) Syntactic Structures (Noam Chomsky) (7) Mathematical Methods in Linguistics (Barbara Partee, Alice G. ter Meulen, Robert Wall) Proof regarding crossing dependencies (adapted from the first edition of Introduction to Automata Theory, Languages, and Computation, by John Hopcroft and Jeffrey Ullman. Note where carets appear that the following character should be taken as superscript): We first capture the general pattern of embedded clauses in Swiss German with the language a^nb^mc^nd^m . We then treat this as the result of intersecting Swiss German with the regular language a*b*c*d*. Now, let L = {a^nb^mc^nd^m | n ≥ 1 and m ≥ 1}. Suppose L is a context-free language, and let p be the pumping length referred to in the pumping lemma for context-free languages (https://en.wikipedia.org/wiki/Pumping_lemma_for_context-free_languages). Consider the string z = a^pb^pc^pd^p. Let z = uvwxy satisfy the conditions of the pumping lemma. Then as |vwx| ≤ p, vx can contain at most two different symbols. Furthermore, if vx contains two different symbols, they must be consecutive, for example, a and b. If vx has only a’s, then uwy has fewer a’s than c’s and is not in L, a contradiction. We proceed similarly if vx consists of only b’s, only c’s, or only d’s. Now suppose that vx has a’s and b’s. Then uwy still has fewer a’s than c’s. A similar contradiction occurs if vx consist of b’s and c’s or c’s and d’s. Since these are the only possibilities, we conclude that L is not context-free. Since context-free languages are closed under intersection with regular languages, and the above intersection is not context-free, Swiss German must be non-context-free. Q.E.D. A proof of the pumping lemma itself can be found in Introduction to the Theory of Computation (among other places). For a discussion of the closure properties of context-free languages, see Mathematical Methods in Linguistics (among other places). Looking forward to next week! -
0:51Natural Language Interaction with ISA Handling, Consequence and Basic Social Reasoning
Natural Language Interaction with ISA Handling, Consequence and Basic Social ReasoningNatural Language Interaction with ISA Handling, Consequence and Basic Social Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal directives. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects). It also rejects directives that it is not obligated to do based on the relative social relationship between the robot and user. -
2:55Machine Learning , Natural Language Processing and Artificial Intelligence all in - IBM Watson
Machine Learning , Natural Language Processing and Artificial Intelligence all in - IBM Watson -
53:26Mod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCESSING
Mod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCESSINGMod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCESSING
Theory of Automata, Formal Languages and Computation by Prof.Kamala Krithivasan,Department of Computer Science and Engineering,IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in -
25:51Natural Language Processing in Python
Natural Language Processing in PythonNatural Language Processing in Python
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool Kit). Shankar had previously presented a comprehensive overview of the NLTK last December at the Python meetup. The Python NLTK is a very powerful collection of libraries that can be applied to a variety of NLP applications such as sentiment analysis. His presentation from last December may be found here (click on Boston Python Meetup Materials) : http://www.shankarambady.com/ http://microsoftcambridge.com/Events/TwitterTextMining/tabid/784/Default.aspx
- Airport code
- Alexander Gode
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Natural Language
ALBUMS
- Natural Language 0098 released: 1998
Natural Language 0098
Released 1998- add to main playlist Play in Full Screen We Are Learning About Blue
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Google Wave: Natural Language Processing
Casey Whitelaw describes the natural language processing behind Google Wave's spelling correction on the deck of the Sydney office. Birds and boat horns for effect. -
NLP Fundamentals: What is Natural Language Processing?
What do Bruce Springsteen and natural language processing have in common? Get the rundown from Expect Labs' Research Director, Marsal Gavaldà. http://www.expectlabs.com TRANSCRIPT: Very often we get asked the question, "what is NLP?" Let me try to give a quick answer. NLP, natural language processing, or actually NLU, natural language understanding, is the set of methodologies and techniques that allow for a computer to make sense of human language. Human language meaning English, Swahili, Chinese; whatever language you speak, as opposed to a computer language, a programming language. So it's the process of analyzing these words and then trying to extract the meaning. For example, if I'm interfacing with an MP3 player I can say, "can you play me a song by Bruce Springsteen where he compl... -
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2... -
Simple Natural Language Interaction with Consequence Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey simple literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself. -
Lecture - 39 Natural Language Processing - I
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in. -
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Devashish Shankar - Deep Learning for Natural Language Processing
Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience. Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising! We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-stat... -
Natural Language Interaction with ISA Handling and Consequence Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects). -
Text By the Bay 2015: Richard Socher, Deep Learning for Natural Language Processing
In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson's Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with MetaMind.io's software. Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models t... -
How Complex is Natural Language? The Chomsky Hierarchy
How can we describe the complexity of linguistic systems? Where does natural language fit in? In this week's episode, we talk about the Chomsky hierarchy: what it captures, what characterizes different kinds of grammars on the hierarchy, and whether we can find grammars that sit higher on the scale than human language. This is Topic #63! This week's tag language: Croatian! Related topics: Happy Little Trees: X' Theory - https://youtu.be/7UOcoQr0hvg Trace Evidence: Syntactic Movement - https://youtu.be/x5iBbSkp8rk Last episode: Quantifying Sets and Toasters: Generalized Quantifiers - https://youtu.be/U1l3C_hmjqM Other of our syntax and morphology videos: Raising the Bar: Raising and Control Verbs - https://youtu.be/SYoYNeaSYrU Organizing Meanings: Morphological Typology - https://youtu... -
Natural Language Interaction with ISA Handling, Consequence and Basic Social Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal directives. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects). It also rejects directives that it is not obligated to do based on the relative social relationship between the robot and user. -
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Mod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCESSING
Theory of Automata, Formal Languages and Computation by Prof.Kamala Krithivasan,Department of Computer Science and Engineering,IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in -
Natural Language Processing in Python
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool Kit). Shankar had previously presented a comprehensive overview of the NLTK last December at the Python meetup. The Python NLTK is a very powerful collection of libraries that can be applied to a variety of NLP applications such as sentiment analysis. His presentation from last December may be found here (click on Boston Python Meetup Materials) : http://www.shankarambady.com/ http://microsoftcambridge.com/Events/TwitterTextMining/tabid/784/Default.aspx -
Speak Like a Doctor - Use Natural Language Processing to Predict Medical Words in R
Using R, natural language processing (NLP), a medical corpus, and a Shiny application, we build an interactive tool to predict what a doctor will say next. Full walkthrough: http://amunategui.github.io/speak-like-a-doctor/ For more in-depth classes on these subjects, check our my Udemy.com classes - specials and discounts: http://amunategui.github.io/udemy/ -
How Natural Language Processing is Changing Research
Aditi Muralidharan demonstrates how natural language processing has not only found its place in the humanities, but has also dramatically sped up the the time it takes to conduct research. -
Stop Words - Natural Language Processing With Python and NLTK p.2
One of the largest elements to any data analysis, natural language processing included, is pre-processing. This is the methodology used to "clean up" and prepare your data for analysis. One of the first steps to pre-processing is to utilize stop-words. Stop words are words that you want to filter out of any analysis. These are words that carry no meaning, or carry conflicting meanings that you simply do not want to deal with. The NLTK module comes with a set of stop words for many language pre-packaged, but you can also easily append more to this list. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.co... -
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Text Classification - Natural Language Processing With Python and NLTK p.11
Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label. To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com -
#2 Building Natural Language Generation Systems
http://www.meetup.com/de/Tomorrows-Language-Technology/events/225093503/ -
Natural Language Generation
Hear our Chief Strategy Scientist Dr. Robert Dale speaking at the SPE Intelligent Energy Conference held in Dubai in 2013 about how we bring data to life through Natural Language Generation (NLG). Learn more about NLG and the Arria NLG Engine that analyses large and diverse data sets and automatically writes tailored reports, with no human intervention, at vast scale and speed. -
Lecture 23: Natural language processing
Lecture 23: Natural language processing This is a lecture video for the Carnegie Mellon course: 'Graduate Artificial Intelligence', Spring 2014. Information about the course is available at http://www.cs.cmu.edu/~zkolter/course/15-780-s14/ -
Grammar Based vs. Natural Language Learning, Part 1
Steve Kaufmann, CEO and founder of LingQ.com and author of The Way of The Linguist, A Language Learning Odyssey, debates with Robyn Matthews, author of Language Logic on how best to learn languages at Sophia Bookstore, Vancouver. Learn a language at: http://www.lingq.com Visit my blog: http://blog.thelinguist.com
Google Wave: Natural Language Processing
- Order: Reorder
- Duration: 5:05
- Updated: 28 May 2009
- views: 114826
Casey Whitelaw describes the natural language processing behind Google Wave's spelling correction on the deck of the Sydney office. Birds and boat horns for eff...
Casey Whitelaw describes the natural language processing behind Google Wave's spelling correction on the deck of the Sydney office. Birds and boat horns for effect.
wn.com/Google Wave Natural Language Processing
Casey Whitelaw describes the natural language processing behind Google Wave's spelling correction on the deck of the Sydney office. Birds and boat horns for effect.
- published: 28 May 2009
- views: 114826
NLP Fundamentals: What is Natural Language Processing?
- Order: Reorder
- Duration: 3:13
- Updated: 19 Aug 2014
- views: 3897
What do Bruce Springsteen and natural language processing have in common? Get the rundown from Expect Labs' Research Director, Marsal Gavaldà. http://www.expect...
What do Bruce Springsteen and natural language processing have in common? Get the rundown from Expect Labs' Research Director, Marsal Gavaldà. http://www.expectlabs.com
TRANSCRIPT:
Very often we get asked the question, "what is NLP?" Let me try to give a quick answer. NLP, natural language processing, or actually NLU, natural language understanding, is the set of methodologies and techniques that allow for a computer to make sense of human language. Human language meaning English, Swahili, Chinese; whatever language you speak, as opposed to a computer language, a programming language. So it's the process of analyzing these words and then trying to extract the meaning. For example, if I'm interfacing with an MP3 player I can say, "can you play me a song by Bruce Springsteen where he complains about having to drive his girlfriend's mother?" Understanding this full sentence means that we have to understand the key part in this case is "play," and that play is a verb as opposed to another example where "I want to see a play," and play is a noun. The context is important, which is why it's difficult for computers to do this process, it's much easier for humans because as we learn, we interact with the world and we subconsciously develop a way to disambiguate and to not get confused by whether "play" is being used as a verb or as a noun.
So what is the song by Bruce Springsteen where he complains about having to drive his girlfriend's mom? Well, it turns out it's "Sherry Darling." The interesting part is that if you have access to all the lyrics, it's not super complicated to have a good indexing of those lyrics and then being able to answer and find the most relevant answers to questions like "which song is the one where something happens?" It's interesting because many times we remember the content of the song, and we don't necessarily remember the title of the song, so it's quite useful to be able to go deeper and index all the lyrics. This is something that is very doable using the MindMeld API, where you can just crawl a website. We internally create our custom knowledge graph and then make it very easy to have a voice-driven interface to this content, voice-driven search and discovery. So you could actually crawl a website with lyrics and then ask this question about this song by Bruce Springsteen where he has to drive his girlfriend's mother and you would say "play" and it would start playing.
wn.com/Nlp Fundamentals What Is Natural Language Processing
What do Bruce Springsteen and natural language processing have in common? Get the rundown from Expect Labs' Research Director, Marsal Gavaldà. http://www.expectlabs.com
TRANSCRIPT:
Very often we get asked the question, "what is NLP?" Let me try to give a quick answer. NLP, natural language processing, or actually NLU, natural language understanding, is the set of methodologies and techniques that allow for a computer to make sense of human language. Human language meaning English, Swahili, Chinese; whatever language you speak, as opposed to a computer language, a programming language. So it's the process of analyzing these words and then trying to extract the meaning. For example, if I'm interfacing with an MP3 player I can say, "can you play me a song by Bruce Springsteen where he complains about having to drive his girlfriend's mother?" Understanding this full sentence means that we have to understand the key part in this case is "play," and that play is a verb as opposed to another example where "I want to see a play," and play is a noun. The context is important, which is why it's difficult for computers to do this process, it's much easier for humans because as we learn, we interact with the world and we subconsciously develop a way to disambiguate and to not get confused by whether "play" is being used as a verb or as a noun.
So what is the song by Bruce Springsteen where he complains about having to drive his girlfriend's mom? Well, it turns out it's "Sherry Darling." The interesting part is that if you have access to all the lyrics, it's not super complicated to have a good indexing of those lyrics and then being able to answer and find the most relevant answers to questions like "which song is the one where something happens?" It's interesting because many times we remember the content of the song, and we don't necessarily remember the title of the song, so it's quite useful to be able to go deeper and index all the lyrics. This is something that is very doable using the MindMeld API, where you can just crawl a website. We internally create our custom knowledge graph and then make it very easy to have a voice-driven interface to this content, voice-driven search and discovery. So you could actually crawl a website with lyrics and then ask this question about this song by Bruce Springsteen where he has to drive his girlfriend's mother and you would say "play" and it would start playing.
- published: 19 Aug 2014
- views: 3897
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
- Order: Reorder
- Duration: 19:54
- Updated: 01 May 2015
- views: 29936
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or ...
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language.
The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text.
NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more!
Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK!
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com
wn.com/Natural Language Processing With Python And Nltk P.1 Tokenizing Words And Sentences
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language.
The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text.
NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more!
Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK!
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com
- published: 01 May 2015
- views: 29936
Simple Natural Language Interaction with Consequence Reasoning
- Order: Reorder
- Duration: 1:02
- Updated: 03 Aug 2015
- views: 272645
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey simple literal commands. It...
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey simple literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself.
wn.com/Simple Natural Language Interaction With Consequence Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey simple literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself.
- published: 03 Aug 2015
- views: 272645
Lecture - 39 Natural Language Processing - I
- Order: Reorder
- Duration: 57:29
- Updated: 16 Oct 2008
- views: 41493
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For mo...
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
wn.com/Lecture 39 Natural Language Processing I
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
- published: 16 Oct 2008
- views: 41493
IBM Watson: Using the Natural Language Classifier in IBM Bluemix!
- Order: Reorder
- Duration: 19:02
- Updated: 04 Dec 2015
- views: 771
I hope you liked this tutorial!
If you did, please make sure to leave a like, comment and subscribe!
Training file: www.tanmaybakshi.com/training.csv
Email: t...
I hope you liked this tutorial!
If you did, please make sure to leave a like, comment and subscribe!
Training file: www.tanmaybakshi.com/training.csv
Email: tajymany@gmail.com
Twitter: @TajyMany
wn.com/Ibm Watson Using The Natural Language Classifier In Ibm Bluemix
Devashish Shankar - Deep Learning for Natural Language Processing
- Order: Reorder
- Duration: 44:43
- Updated: 18 Jul 2015
- views: 2841
Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired t...
Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience.
Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising!
We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart.
In this talk, we plan to cover the following:
Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.
New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets - up from 85% by statistical models)
Share some of the learnings we have had while deploying these in Flipkart!
Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html
We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute!
wn.com/Devashish Shankar Deep Learning For Natural Language Processing
Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience.
Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising!
We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart.
In this talk, we plan to cover the following:
Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.
New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets - up from 85% by statistical models)
Share some of the learnings we have had while deploying these in Flipkart!
Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html
We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute!
- published: 18 Jul 2015
- views: 2841
Natural Language Interaction with ISA Handling and Consequence Reasoning
- Order: Reorder
- Duration: 1:07
- Updated: 03 Aug 2015
- views: 52849
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-litera...
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects).
wn.com/Natural Language Interaction With Isa Handling And Consequence Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal commands. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects).
- published: 03 Aug 2015
- views: 52849
Text By the Bay 2015: Richard Socher, Deep Learning for Natural Language Processing
- Order: Reorder
- Duration: 45:14
- Updated: 10 Jun 2015
- views: 5436
In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problem...
In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson's Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with MetaMind.io's software.
Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 'Magic Grant' from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.
wn.com/Text By The Bay 2015 Richard Socher, Deep Learning For Natural Language Processing
In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson's Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with MetaMind.io's software.
Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 'Magic Grant' from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.
- published: 10 Jun 2015
- views: 5436
How Complex is Natural Language? The Chomsky Hierarchy
- Order: Reorder
- Duration: 9:39
- Updated: 02 Mar 2016
- views: 134
How can we describe the complexity of linguistic systems? Where does natural language fit in? In this week's episode, we talk about the Chomsky hierarchy: what ...
How can we describe the complexity of linguistic systems? Where does natural language fit in? In this week's episode, we talk about the Chomsky hierarchy: what it captures, what characterizes different kinds of grammars on the hierarchy, and whether we can find grammars that sit higher on the scale than human language.
This is Topic #63!
This week's tag language: Croatian!
Related topics:
Happy Little Trees: X' Theory - https://youtu.be/7UOcoQr0hvg
Trace Evidence: Syntactic Movement - https://youtu.be/x5iBbSkp8rk
Last episode:
Quantifying Sets and Toasters: Generalized Quantifiers - https://youtu.be/U1l3C_hmjqM
Other of our syntax and morphology videos:
Raising the Bar: Raising and Control Verbs - https://youtu.be/SYoYNeaSYrU
Organizing Meanings: Morphological Typology - https://youtu.be/Ts2DS0ZsTyo
Referential Treatment: Binding Theory - https://youtu.be/9sqm_cex4kA
Also, if you'd like to know more about the Chomsky Hierarchy and its impact on computer science, Computerphile's had a few videos about them:
- Their episode on the hierarchy: https://www.youtube.com/watch?v=224plb3bCog.
- Their episode about finite-state machines: https://www.youtube.com/watch?v=vhiiia1_hC4.
- And their episode about how finite-state machines relate to grammar: https://www.youtube.com/watch?v=RjOCRYdg8BY.
Find us on all the social media worlds:
Tumblr: http://thelingspace.tumblr.com/
Twitter: http://twitter.com/TheLingSpace
Facebook: http://www.facebook.com/thelingspace/
And at our website, http://www.thelingspace.com/ !
You can also find our store at the website, https://thelingspace.storenvy.com/
Our website also has extra content about this week's topic at http://www.thelingspace.com/episode-63/
(Or it will by Thursday morning.)
We also have forums to discuss this episode, and linguistics more generally.
Sources:
(1) I-Language (1st Edition, Daniela isac & Charles Reiss)
(2) Introduction to the Theory of Computation (3rd Edition, Michael Sipser)
(3) Mathematical Logic for Computer Science (3rd Edition, Mordechai Ben-Ari)
(4) Evidence Against the Context-Freeness of Natural Language (Stuart Shieber - http://www.eecs.harvard.edu/~shieber/Biblio/Papers/shieber85.pdf)
(5) https://en.wikipedia.org/wiki/Chomsky_hierarchy
(6) Syntactic Structures (Noam Chomsky)
(7) Mathematical Methods in Linguistics (Barbara Partee, Alice G. ter Meulen, Robert Wall)
Proof regarding crossing dependencies (adapted from the first edition of Introduction to Automata Theory, Languages, and Computation, by John Hopcroft and Jeffrey Ullman. Note where carets appear that the following character should be taken as superscript):
We first capture the general pattern of embedded clauses in Swiss German with the language a^nb^mc^nd^m . We then treat this as the result of intersecting Swiss German with the regular language a*b*c*d*.
Now, let L = {a^nb^mc^nd^m | n ≥ 1 and m ≥ 1}. Suppose L is a context-free language, and let p be the pumping length referred to in the pumping lemma for context-free languages (https://en.wikipedia.org/wiki/Pumping_lemma_for_context-free_languages). Consider the string z = a^pb^pc^pd^p. Let z = uvwxy satisfy the conditions of the pumping lemma. Then as |vwx| ≤ p, vx can contain at most two different symbols. Furthermore, if vx contains two different symbols, they must be consecutive, for example, a and b. If vx has only a’s, then uwy has fewer a’s than c’s and is not in L, a contradiction. We proceed similarly if vx consists of only b’s, only c’s, or only d’s. Now suppose that vx has a’s and b’s. Then uwy still has fewer a’s than c’s. A similar contradiction occurs if vx consist of b’s and c’s or c’s and d’s. Since these are the only possibilities, we conclude that L is not context-free.
Since context-free languages are closed under intersection with regular languages, and the above intersection is not context-free, Swiss German must be non-context-free.
Q.E.D.
A proof of the pumping lemma itself can be found in Introduction to the Theory of Computation (among other places). For a discussion of the closure properties of context-free languages, see Mathematical Methods in Linguistics (among other places).
Looking forward to next week!
wn.com/How Complex Is Natural Language The Chomsky Hierarchy
How can we describe the complexity of linguistic systems? Where does natural language fit in? In this week's episode, we talk about the Chomsky hierarchy: what it captures, what characterizes different kinds of grammars on the hierarchy, and whether we can find grammars that sit higher on the scale than human language.
This is Topic #63!
This week's tag language: Croatian!
Related topics:
Happy Little Trees: X' Theory - https://youtu.be/7UOcoQr0hvg
Trace Evidence: Syntactic Movement - https://youtu.be/x5iBbSkp8rk
Last episode:
Quantifying Sets and Toasters: Generalized Quantifiers - https://youtu.be/U1l3C_hmjqM
Other of our syntax and morphology videos:
Raising the Bar: Raising and Control Verbs - https://youtu.be/SYoYNeaSYrU
Organizing Meanings: Morphological Typology - https://youtu.be/Ts2DS0ZsTyo
Referential Treatment: Binding Theory - https://youtu.be/9sqm_cex4kA
Also, if you'd like to know more about the Chomsky Hierarchy and its impact on computer science, Computerphile's had a few videos about them:
- Their episode on the hierarchy: https://www.youtube.com/watch?v=224plb3bCog.
- Their episode about finite-state machines: https://www.youtube.com/watch?v=vhiiia1_hC4.
- And their episode about how finite-state machines relate to grammar: https://www.youtube.com/watch?v=RjOCRYdg8BY.
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Our website also has extra content about this week's topic at http://www.thelingspace.com/episode-63/
(Or it will by Thursday morning.)
We also have forums to discuss this episode, and linguistics more generally.
Sources:
(1) I-Language (1st Edition, Daniela isac & Charles Reiss)
(2) Introduction to the Theory of Computation (3rd Edition, Michael Sipser)
(3) Mathematical Logic for Computer Science (3rd Edition, Mordechai Ben-Ari)
(4) Evidence Against the Context-Freeness of Natural Language (Stuart Shieber - http://www.eecs.harvard.edu/~shieber/Biblio/Papers/shieber85.pdf)
(5) https://en.wikipedia.org/wiki/Chomsky_hierarchy
(6) Syntactic Structures (Noam Chomsky)
(7) Mathematical Methods in Linguistics (Barbara Partee, Alice G. ter Meulen, Robert Wall)
Proof regarding crossing dependencies (adapted from the first edition of Introduction to Automata Theory, Languages, and Computation, by John Hopcroft and Jeffrey Ullman. Note where carets appear that the following character should be taken as superscript):
We first capture the general pattern of embedded clauses in Swiss German with the language a^nb^mc^nd^m . We then treat this as the result of intersecting Swiss German with the regular language a*b*c*d*.
Now, let L = {a^nb^mc^nd^m | n ≥ 1 and m ≥ 1}. Suppose L is a context-free language, and let p be the pumping length referred to in the pumping lemma for context-free languages (https://en.wikipedia.org/wiki/Pumping_lemma_for_context-free_languages). Consider the string z = a^pb^pc^pd^p. Let z = uvwxy satisfy the conditions of the pumping lemma. Then as |vwx| ≤ p, vx can contain at most two different symbols. Furthermore, if vx contains two different symbols, they must be consecutive, for example, a and b. If vx has only a’s, then uwy has fewer a’s than c’s and is not in L, a contradiction. We proceed similarly if vx consists of only b’s, only c’s, or only d’s. Now suppose that vx has a’s and b’s. Then uwy still has fewer a’s than c’s. A similar contradiction occurs if vx consist of b’s and c’s or c’s and d’s. Since these are the only possibilities, we conclude that L is not context-free.
Since context-free languages are closed under intersection with regular languages, and the above intersection is not context-free, Swiss German must be non-context-free.
Q.E.D.
A proof of the pumping lemma itself can be found in Introduction to the Theory of Computation (among other places). For a discussion of the closure properties of context-free languages, see Mathematical Methods in Linguistics (among other places).
Looking forward to next week!
- published: 02 Mar 2016
- views: 134
Natural Language Interaction with ISA Handling, Consequence and Basic Social Reasoning
- Order: Reorder
- Duration: 0:51
- Updated: 03 Aug 2015
- views: 31201
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-litera...
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal directives. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects). It also rejects directives that it is not obligated to do based on the relative social relationship between the robot and user.
wn.com/Natural Language Interaction With Isa Handling, Consequence And Basic Social Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language based interaction. The robot is able to obey both literal and non-literal directives. It is also able to appropriately reject commands based on reasoning regarding action effects. In this case, it rejects commands that can possibly result in harm to itself (walking into solid objects). It also rejects directives that it is not obligated to do based on the relative social relationship between the robot and user.
- published: 03 Aug 2015
- views: 31201
Machine Learning , Natural Language Processing and Artificial Intelligence all in - IBM Watson
- Order: Reorder
- Duration: 2:55
- Updated: 07 Aug 2015
- views: 1249
Mod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCESSING
- Order: Reorder
- Duration: 53:26
- Updated: 05 Oct 2011
- views: 164290
Theory of Automata, Formal Languages and Computation by Prof.Kamala Krithivasan,Department of Computer Science and Engineering,IIT Madras. For more details on N...
Theory of Automata, Formal Languages and Computation by Prof.Kamala Krithivasan,Department of Computer Science and Engineering,IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
wn.com/Mod 01 Lec 01 Grammars And Natural Language Processing
Theory of Automata, Formal Languages and Computation by Prof.Kamala Krithivasan,Department of Computer Science and Engineering,IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in
- published: 05 Oct 2011
- views: 164290
Natural Language Processing in Python
- Order: Reorder
- Duration: 25:51
- Updated: 03 Jul 2011
- views: 23624
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool Kit). Shankar had previously presented a comprehensive overview of t...
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool Kit). Shankar had previously presented a comprehensive overview of the NLTK last December at the Python meetup. The Python NLTK is a very powerful collection of libraries that can be applied to a variety of NLP applications such as sentiment analysis. His presentation from last December may be found here (click on Boston Python Meetup Materials) : http://www.shankarambady.com/
http://microsoftcambridge.com/Events/TwitterTextMining/tabid/784/Default.aspx
wn.com/Natural Language Processing In Python
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool Kit). Shankar had previously presented a comprehensive overview of the NLTK last December at the Python meetup. The Python NLTK is a very powerful collection of libraries that can be applied to a variety of NLP applications such as sentiment analysis. His presentation from last December may be found here (click on Boston Python Meetup Materials) : http://www.shankarambady.com/
http://microsoftcambridge.com/Events/TwitterTextMining/tabid/784/Default.aspx
- published: 03 Jul 2011
- views: 23624
Speak Like a Doctor - Use Natural Language Processing to Predict Medical Words in R
- Order: Reorder
- Duration: 31:13
- Updated: 24 Nov 2015
- views: 298
Using R, natural language processing (NLP), a medical corpus, and a Shiny application, we build an interactive tool to predict what a doctor will say next.
Ful...
Using R, natural language processing (NLP), a medical corpus, and a Shiny application, we build an interactive tool to predict what a doctor will say next.
Full walkthrough: http://amunategui.github.io/speak-like-a-doctor/
For more in-depth classes on these subjects, check our my Udemy.com classes - specials and discounts:
http://amunategui.github.io/udemy/
wn.com/Speak Like A Doctor Use Natural Language Processing To Predict Medical Words In R
Using R, natural language processing (NLP), a medical corpus, and a Shiny application, we build an interactive tool to predict what a doctor will say next.
Full walkthrough: http://amunategui.github.io/speak-like-a-doctor/
For more in-depth classes on these subjects, check our my Udemy.com classes - specials and discounts:
http://amunategui.github.io/udemy/
- published: 24 Nov 2015
- views: 298
How Natural Language Processing is Changing Research
- Order: Reorder
- Duration: 5:57
- Updated: 20 May 2011
- views: 6034
Aditi Muralidharan demonstrates how natural language processing has not only found its place in the humanities, but has also dramatically sped up the the time i...
Aditi Muralidharan demonstrates how natural language processing has not only found its place in the humanities, but has also dramatically sped up the the time it takes to conduct research.
wn.com/How Natural Language Processing Is Changing Research
Aditi Muralidharan demonstrates how natural language processing has not only found its place in the humanities, but has also dramatically sped up the the time it takes to conduct research.
- published: 20 May 2011
- views: 6034
Stop Words - Natural Language Processing With Python and NLTK p.2
- Order: Reorder
- Duration: 7:49
- Updated: 02 May 2015
- views: 10860
One of the largest elements to any data analysis, natural language processing included, is pre-processing. This is the methodology used to "clean up" and prepar...
One of the largest elements to any data analysis, natural language processing included, is pre-processing. This is the methodology used to "clean up" and prepare your data for analysis.
One of the first steps to pre-processing is to utilize stop-words. Stop words are words that you want to filter out of any analysis. These are words that carry no meaning, or carry conflicting meanings that you simply do not want to deal with.
The NLTK module comes with a set of stop words for many language pre-packaged, but you can also easily append more to this list.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com
wn.com/Stop Words Natural Language Processing With Python And Nltk P.2
One of the largest elements to any data analysis, natural language processing included, is pre-processing. This is the methodology used to "clean up" and prepare your data for analysis.
One of the first steps to pre-processing is to utilize stop-words. Stop words are words that you want to filter out of any analysis. These are words that carry no meaning, or carry conflicting meanings that you simply do not want to deal with.
The NLTK module comes with a set of stop words for many language pre-packaged, but you can also easily append more to this list.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com
- published: 02 May 2015
- views: 10860
What is natural language processing?
- Order: Reorder
- Duration: 1:32
- Updated: 16 Nov 2012
- views: 5800
Text Classification - Natural Language Processing With Python and NLTK p.11
- Order: Reorder
- Duration: 11:41
- Updated: 12 May 2015
- views: 5976
Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is whe...
Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label.
To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com
wn.com/Text Classification Natural Language Processing With Python And Nltk P.11
Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label.
To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list;=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index;=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com
- published: 12 May 2015
- views: 5976
#2 Building Natural Language Generation Systems
- Order: Reorder
- Duration: 53:50
- Updated: 23 Sep 2015
- views: 256
http://www.meetup.com/de/Tomorrows-Language-Technology/events/225093503/
http://www.meetup.com/de/Tomorrows-Language-Technology/events/225093503/
wn.com/2 Building Natural Language Generation Systems
http://www.meetup.com/de/Tomorrows-Language-Technology/events/225093503/
- published: 23 Sep 2015
- views: 256
Natural Language Generation
- Order: Reorder
- Duration: 10:56
- Updated: 06 Oct 2014
- views: 753
Hear our Chief Strategy Scientist Dr. Robert Dale speaking at the SPE Intelligent Energy Conference held in Dubai in 2013 about how we bring data to life throug...
Hear our Chief Strategy Scientist Dr. Robert Dale speaking at the SPE Intelligent Energy Conference held in Dubai in 2013 about how we bring data to life through Natural Language Generation (NLG).
Learn more about NLG and the Arria NLG Engine that analyses large and diverse data sets and automatically writes tailored reports, with no human intervention, at vast scale and speed.
wn.com/Natural Language Generation
Hear our Chief Strategy Scientist Dr. Robert Dale speaking at the SPE Intelligent Energy Conference held in Dubai in 2013 about how we bring data to life through Natural Language Generation (NLG).
Learn more about NLG and the Arria NLG Engine that analyses large and diverse data sets and automatically writes tailored reports, with no human intervention, at vast scale and speed.
- published: 06 Oct 2014
- views: 753
Lecture 23: Natural language processing
- Order: Reorder
- Duration: 75:27
- Updated: 18 Apr 2014
- views: 2833
Lecture 23: Natural language processing
This is a lecture video for the Carnegie Mellon course: 'Graduate Artificial Intelligence', Spring 2014.
Information...
Lecture 23: Natural language processing
This is a lecture video for the Carnegie Mellon course: 'Graduate Artificial Intelligence', Spring 2014.
Information about the course is available at http://www.cs.cmu.edu/~zkolter/course/15-780-s14/
wn.com/Lecture 23 Natural Language Processing
Lecture 23: Natural language processing
This is a lecture video for the Carnegie Mellon course: 'Graduate Artificial Intelligence', Spring 2014.
Information about the course is available at http://www.cs.cmu.edu/~zkolter/course/15-780-s14/
- published: 18 Apr 2014
- views: 2833
Grammar Based vs. Natural Language Learning, Part 1
- Order: Reorder
- Duration: 10:00
- Updated: 12 May 2008
- views: 20841
Steve Kaufmann, CEO and founder of LingQ.com and author of The Way of The Linguist, A Language Learning Odyssey, debates with Robyn Matthews, author of Language...
Steve Kaufmann, CEO and founder of LingQ.com and author of The Way of The Linguist, A Language Learning Odyssey, debates with Robyn Matthews, author of Language Logic on how best to learn languages at Sophia Bookstore, Vancouver.
Learn a language at: http://www.lingq.com
Visit my blog: http://blog.thelinguist.com
wn.com/Grammar Based Vs. Natural Language Learning, Part 1
Steve Kaufmann, CEO and founder of LingQ.com and author of The Way of The Linguist, A Language Learning Odyssey, debates with Robyn Matthews, author of Language Logic on how best to learn languages at Sophia Bookstore, Vancouver.
Learn a language at: http://www.lingq.com
Visit my blog: http://blog.thelinguist.com
- published: 12 May 2008
- views: 20841
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close fullscreen
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5:05
Google Wave: Natural Language Processing
Casey Whitelaw describes the natural language processing behind Google Wave's spelling cor...
published: 28 May 2009
Google Wave: Natural Language Processing
Google Wave: Natural Language Processing
- Report rights infringement
- published: 28 May 2009
- views: 114826
3:13
NLP Fundamentals: What is Natural Language Processing?
What do Bruce Springsteen and natural language processing have in common? Get the rundown ...
published: 19 Aug 2014
NLP Fundamentals: What is Natural Language Processing?
NLP Fundamentals: What is Natural Language Processing?
- Report rights infringement
- published: 19 Aug 2014
- views: 3897
19:54
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
Natural Language Processing is the task we give computers to read and understand (process)...
published: 01 May 2015
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
- Report rights infringement
- published: 01 May 2015
- views: 29936
1:02
Simple Natural Language Interaction with Consequence Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language ba...
published: 03 Aug 2015
Simple Natural Language Interaction with Consequence Reasoning
Simple Natural Language Interaction with Consequence Reasoning
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- published: 03 Aug 2015
- views: 272645
57:29
Lecture - 39 Natural Language Processing - I
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, De...
published: 16 Oct 2008
Lecture - 39 Natural Language Processing - I
Lecture - 39 Natural Language Processing - I
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- published: 16 Oct 2008
- views: 41493
19:02
IBM Watson: Using the Natural Language Classifier in IBM Bluemix!
I hope you liked this tutorial!
If you did, please make sure to leave a like, comment and...
published: 04 Dec 2015
IBM Watson: Using the Natural Language Classifier in IBM Bluemix!
IBM Watson: Using the Natural Language Classifier in IBM Bluemix!
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- published: 04 Dec 2015
- views: 771
44:43
Devashish Shankar - Deep Learning for Natural Language Processing
Much of the Text Mining needed in real-life boils down to Text Classification: be it prior...
published: 18 Jul 2015
Devashish Shankar - Deep Learning for Natural Language Processing
Devashish Shankar - Deep Learning for Natural Language Processing
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- published: 18 Jul 2015
- views: 2841
1:07
Natural Language Interaction with ISA Handling and Consequence Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language ba...
published: 03 Aug 2015
Natural Language Interaction with ISA Handling and Consequence Reasoning
Natural Language Interaction with ISA Handling and Consequence Reasoning
- Report rights infringement
- published: 03 Aug 2015
- views: 52849
45:14
Text By the Bay 2015: Richard Socher, Deep Learning for Natural Language Processing
In this talk, I will describe deep learning algorithms that learn representations for lang...
published: 10 Jun 2015
Text By the Bay 2015: Richard Socher, Deep Learning for Natural Language Processing
Text By the Bay 2015: Richard Socher, Deep Learning for Natural Language Processing
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- published: 10 Jun 2015
- views: 5436
9:39
How Complex is Natural Language? The Chomsky Hierarchy
How can we describe the complexity of linguistic systems? Where does natural language fit ...
published: 02 Mar 2016
How Complex is Natural Language? The Chomsky Hierarchy
How Complex is Natural Language? The Chomsky Hierarchy
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- published: 02 Mar 2016
- views: 134
0:51
Natural Language Interaction with ISA Handling, Consequence and Basic Social Reasoning
Here we see the DIARC architecture controlling a Nao robot in a simple natural language ba...
published: 03 Aug 2015
Natural Language Interaction with ISA Handling, Consequence and Basic Social Reasoning
Natural Language Interaction with ISA Handling, Consequence and Basic Social Reasoning
- Report rights infringement
- published: 03 Aug 2015
- views: 31201
2:55
Machine Learning , Natural Language Processing and Artificial Intelligence all in - IBM Watson
Working principle of An AI machine built by IBM
published: 07 Aug 2015
Machine Learning , Natural Language Processing and Artificial Intelligence all in - IBM Watson
Machine Learning , Natural Language Processing and Artificial Intelligence all in - IBM Watson
- Report rights infringement
- published: 07 Aug 2015
- views: 1249
53:26
Mod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCESSING
Theory of Automata, Formal Languages and Computation by Prof.Kamala Krithivasan,Department...
published: 05 Oct 2011
Mod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCESSING
Mod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCESSING
- Report rights infringement
- published: 05 Oct 2011
- views: 164290
25:51
Natural Language Processing in Python
Shankar Ambady of Session M will give a tutorial on the Python NLTK (Natural Language Tool...
published: 03 Jul 2011
Natural Language Processing in Python
Natural Language Processing in Python
- Report rights infringement
- published: 03 Jul 2011
- views: 23624
Google Wave: Natural Language Processing...
NLP Fundamentals: What is Natural Language Process...
Natural Language Processing With Python and NLTK p...
Simple Natural Language Interaction with Consequen...
Lecture - 39 Natural Language Processing - I...
IBM Watson: Using the Natural Language Classifier ...
Devashish Shankar - Deep Learning for Natural Lang...
Natural Language Interaction with ISA Handling and...
Text By the Bay 2015: Richard Socher, Deep Learnin...
How Complex is Natural Language? The Chomsky Hiera...
Natural Language Interaction with ISA Handling, Co...
Machine Learning , Natural Language Processing an...
Mod-01 Lec-01 GRAMMARS AND NATURAL LANGUAGE PROCES...
Natural Language Processing in Python...
Speak Like a Doctor - Use Natural Language Process...
How Natural Language Processing is Changing Resear...
Stop Words - Natural Language Processing With Pyth...
What is natural language processing?...
Text Classification - Natural Language Processing ...
#2 Building Natural Language Generation Systems...
Natural Language Generation...
Lecture 23: Natural language processing...
Grammar Based vs. Natural Language Learning, Part ...
photo: WN / Aruna Mirasdar
Porn and the Threat to Virility
Edit Time Magazine 31 Mar 2016
Noah Church is a 26-year-old part-time wildland firefighter in Portland, Ore. When he was 9, he found naked pictures on the Internet ... Often ... He hypothesizes that the visual nature of porn makes it particularly appealing for the brain ... “However, they also have the potential to desensitize you to normal, natural things and ultimately rob you of the one thing you thought they would give you, the ability to experience pleasure.”....
photo: WN / Bhaskar Mallick
India bridge collapse: Kolkata rescue efforts under way
Edit Al Jazeera 01 Apr 2016
Emergency workers in Kolkata continued to search for survivors underneath a collapsed overpass in a congested area of the eastern city, as the construction firm building the flyover was being investigated. At least 21 people were killed and over a dozens were still trapped under the rubble of the under-construction overpass that collapsed on Thursday ... "The condition is pathetic ... And the flyover is built in between residential buildings....
photo: Via YouTube
Google's April Fools' joke went so wrong that it might have cost someone their job
Edit TechRadar 01 Apr 2016
We try to avoid as much of April Fools' day as possible on techradar. In fact, as a rule, we'd advise sleeping in until midday, that way you'll avoid all the terrible "jokes" entirely. We can imagine there are a few people at Google who would like to go to bed and start the day again though after its latest feature went south very quickly ... Not so funny ... Meanwhile, Abdus Salam wrote ... ....
photo: AP / Bikas Das
At Least 21 Confirmed Dead, Scores Injured, Missing In Kolkata Overpass Collapse
Edit WorldNews.com 31 Mar 2016
Rescuers in Kolkata used saws, small cranes and bare hands to dig through the wreckage after a long section of a road overpass under construction collapsed in a crowded Indian neighborhood Thursday killing 21 people and leave scores of injured, the Belfast Telegraph reported Thursday ... “[It] came down with a huge crashing sound. I left my cup of tea and ran. I was crying at the spot.” – Yogesh Sharma, eyewitness to the overpass collapse....
photo: Twitter
Kim Kardashian, Emily Ratajkowski post topless bathroom pic
Edit New York Daily News 30 Mar 2016
Kim Kardashian has added a friend to her set of scandalous selfies ... ....
« back to news headlines
Build 2016: Windows 10 anniversary update, AI, UWP and bots
Edit The Times of India 01 Apr 2016
"Human language is the new UI layer, bots are like new applications, and digital assistants are meta apps ... On the other hand, the bot framework can be used by developers — programming in any language — to build intelligent bots that enable customers to chat using natural language on a wide variety of platforms including text/SMS, Office 365, Skype, Slack, the Web and more....
Hilton and IBM Pilot “Connie,” The World’s First Watson-Enabled Hotel Concierge (IBM China/Hong Kong Limited)
Edit Public Technologies 01 Apr 2016
Connie uses a combination of Watson APIs, including Dialog, Speech to Text, Text to Speech and Natural Language Classifier, to enable it to greet guests upon arrival and to answer questions about hotel amenities, services and hours of operation....
Best iPad apps 2016: download these now
Edit TechRadar 01 Apr 2016
The destructive nature of effects and editing is a pity - you can't later adjust something you changed a while ago, only undo ... The best bit, though, is Fantastical's natural-language input, where you can type an event and watch it build as you add details, such as times and locations.Flipboard (free) ... When you're told you can control the forces of nature with your fingertips that probably puts you more in mind of a game than a book....
Apple turns 40 - here are its highs and lows throughout the years
Edit TechRadar 01 Apr 2016
Introduction. Apple is the most valuable brand in the world ... although to others it means something quite the opposite ... So come, take our hand ... Happy Birthday, Apple.1 ... Simply put ... "An iPod ... 7 ... 13 ... Siri isn't just about a single search but interpreting a spoken, natural-language interaction and collating responses from multiple sources – having a conversation (although most of the time this descends into trying to find a Siri 'joke' response ... ....
Microsoft’s new AI tools help developers build smart apps and bots [US]
Edit Ars Technica 31 Mar 2016It’s man with machines, not man vs machines: Satya Nadella at Build 2016
Edit The Times of India 31 Mar 2016
"Human language is the new UI layer, bots are like new applications, and digital assistants are meta apps ... On the other hand, the bot framework can be used by developers — programming in any language — to build intelligent bots that enable customers to chat using natural language on a wide variety of platforms including text/SMS, Office 365, Skype, Slack, the Web and more....
Now anyone can build their own version of Microsoft's racist, sexist chatbot Tay
Edit The Guardian 31 Mar 2016
“Bots are the new apps,” Nadella said ... Nadella said. “As an industry, we are on the cusp of a new frontier that pairs the power of natural human language with advanced machine intelligence.” Going to the users ... Slack’s built-in Slackbot acts as the service’s butler, while Google’s Now, Amazon’s Alexa and Apple’s Siri offer similar features through both spoken natural language ... ....
Would you trust Microsoft's AI to caption your selfies? Firm reveals ‘CaptionBot’ you can try ...
Edit The Daily Mail 31 Mar 2016
Microsoft's CaptionBot analyzes pictures and write captions on what it 'sees'. Using computer vision and natural language, it can work out gender, celebrities and facial expressions ... ....
Microsoft's Spooky New CaptionBot Can Accurately Describe Your Photos - Sometimes
Edit Forbes 31 Mar 2016
Microsoft’s CaptionBot uses computer vision and natural language processing to describe any photo, just as a human would. It’s very impressive, although it doesn't always get things right. Launched at this year’s Build conference, CaptionBot is a new web-based app designed to showcase Microsoft’s Cognitive Services APIs - tools created to [...] ... ....
Landskipping review: Anna Pavord's audit of the English cultural landscape
Edit Sydney Morning Herald 31 Mar 2016
Widely known for her bestselling book The Tulip, and for her writing on gardening in Britain's Independent, Anna Pavord is also the author of the remarkable The Naming of Names, a rather epic piece of personable scholarship looking at the way human cultures have attempted to order nature through language ... of "natural" aspects of the land....
Microsoft outlines intelligence vision and announces new innovations for Windows 10 (Microsoft Hong Kong Limited)
Edit Public Technologies 31 Mar 2016
'As an industry, we are on the cusp of a new frontier that pairs the power of natural human language with advanced machine intelligence,' said Satya Nadella, CEO, Microsoft ... The second, the Microsoft Bot Framework, can be used by developers - programming in any language - to build intelligent bots that enable customers to chat using natural language ......
Here's why Cortana leaves Siri and Google Now in the dust
Edit TechRadar 31 Mar 2016
Microsoft Cognitive Services, a set of APIs that let computers understand humans in more natural ... One of the goals that Microsoft outlined at Build was to enable users to interact with their computers using natural language. This is achieved by collecting vast amounts of natural language data which is then processed, broken down, and learnt from....
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