- published: 07 Jan 2021
- views: 553
-
remove the playlistSo Close (disambiguation)
- remove the playlistSo Close (disambiguation)
So Close (disambiguation)
So Close is a Hong Kong action film.
So Close may also refer to:
See also
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/So_Close_(disambiguation)
So Close
So Close is a 2002 Hong Kong action film directed by Corey Yuen, starring Shu Qi, Zhao Wei and Karen Mok. The film's English title is derived from The Carpenters' song "Close to You", which has a prominent role in the film.
Plot
Lynn and her sister Sue are computer hackers, assassins and espionage specialists who use their late father's secret satellite technology to gain an advantage over their rivals and law enforcement agents. At the beginning of the film, they infiltrate a high security building and assassinate Chow Lui, the chairman of a top company in Hong Kong.
After their successful mission, a police inspector named Kong Yat-hung is assigned to investigate the case and she manages to track down the assassins. In the meantime, Chow Lui's younger brother Chow Nung, who hired Lynn and Sue to kill his brother so that he can become the chairman, wants to kill the assassins to silence them. The cat-and-mouse chase becomes more complicated as both the police and the thugs are out to get Lynn and Sue.
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/So_Close
So Close (Hall & Oates song)
"So Close" is a 1990 song by American pop duo Hall & Oates. It was written by Daryl Hall, George Green and Rick Giles, Jon Bon Jovi & Danny Kortchmar, as well as produced by Bon Jovi and Kortchmar. The song was released as the lead single from the Change of Season album and peaked at number 11 in the United States and number four in Canada. An acoustic version of the song also appears on the album and as a B-side of the single.
Release and reception
"So Close" was released as a single in September 1990, and it debuted on the U.S. Billboard Hot 100 chart the same month. It peaked at number 11 in December and spent 19 weeks on the chart. "So Close" was Hall & Oates' 29th and most recent single to reach the Top 40 section of the chart. The song also reached number 11 on the Hot 100 Airplay chart, number 14 on the Singles Sales chart, and number six on the Adult Contemporary chart. The single peaked at number four in Canada and ranked number 36 on RPM magazine's year-end chart. It was less commercially successful in the United Kingdom, where it spent a sole week on the UK Singles Chart at number 69.
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/So_Close_(Hall_&_Oates_song)
Podcasts:
-
Disambiguation – Linking Data Science and Engineering | NLP Summit 2020
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/ Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/ Watch all NLP Summit 2020 sessions: https://www.nlpsummit.org/ Disambiguation or Entity Linking is the assignment of a knowledge base identifier (Wikidata, Wikipedia) to a named entity. Our goal was to improve an MVP model by adding newly created knowledge while maintaining competitive F1 scores. Taking an entity linking model from MVP into production in a spaCy-native pipeline architecture posed several data science and engineering challenges, such as hyperparameter estimation and knowledge enhancement, which we addressed by taking advantage of the engineering tools Docker and Kubernetes to semi-automate training as a...
published: 07 Jan 2021 -
093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph. Speakers: Federica Ventruto, Alessia Melania Lonoce Format: Full Session 30-45 min Level: Advanced Topics: #KnowledgeGraph, #MachineLearning...
published: 30 Nov 2022 -
chess king sacrifice
Credits to: Chessbase India
published: 01 Dec 2020 -
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, appearance estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) t...
published: 27 Oct 2022 -
ApplyAI Hands-on in NLP: Word Disambiguation and Automatic Summarization
You can find the Google Drive folder with the notebooks here: https://drive.google.com/drive/folders/1paIso1fqasLblXgjvkzOwEns4cO81ipc
published: 09 May 2020 -
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee | Code BEAM America 2021
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/ And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee - Senior Software Engineer at Interfolio ABSTRACT Synonymity and homonymity make name disambiguation difficult. To ease this difficulty, I combined two unmaintained Elixir string comparison libraries and added preprocessing and a double metaphone algorithm. The result is a comprehensive map of scores for pattern identification and machine learning. This talk will address the pre-processing, algorithms, and scoring as well as the strengths and limitations. A live demonstration of scoring will allow us to identify patterns. We end with a discussion of how to gain further benefits from the scores. OBJECTIVES: To introduce the ...
published: 16 Sep 2022 -
Disambiguation Of Ambiguous Terms.
Ambiguity is tough for computers. It can be tough for humans to disambiguate ambiguous terms, but we tend to be pretty good at it. To be great at it you need context. That is what Stremor's Liquid helium does. It figures out the context of words and disambiguates them.
published: 05 Apr 2013 -
Word Sense Disambiguation
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/ Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sense%20Disambiguation.pdf Twitter: @NatalieParde
published: 28 Dec 2020 -
disambiguation
published: 04 Oct 2019 -
Underoath - Disambiguation - A Divine Eradication
All music rights go to Underoath, Solid State Records, and Tooth and Nail Records. Album line up: 1. In Division 2. Catch Myself Catching Myself 3. Paper Lung 4. Illuminator 5. Driftwood 6. A Divine Eradication 7. Who Will Guard the Guardians 8. Reversal 9. Vacant Mouth 10. My Deteriorating Incline 11. In Completion
published: 02 Nov 2010
29:09
Disambiguation – Linking Data Science and Engineering | NLP Summit 2020
- Order: Reorder
- Duration: 29:09
- Uploaded Date: 07 Jan 2021
- views: 553
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/
Register for NLP Summit 2021: https://www.nlpsummit.org/2021...
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/
Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/
Watch all NLP Summit 2020 sessions: https://www.nlpsummit.org/
Disambiguation or Entity Linking is the assignment of a knowledge base identifier (Wikidata, Wikipedia) to a named entity. Our goal was to improve an MVP model by adding newly created knowledge while maintaining competitive F1 scores.
Taking an entity linking model from MVP into production in a spaCy-native pipeline architecture posed several data science and engineering challenges, such as hyperparameter estimation and knowledge enhancement, which we addressed by taking advantage of the engineering tools Docker and Kubernetes to semi-automate training as an on-demand job.
We also discuss some of our learnings and process improvements that were needed to strike a balance between data science goals and engineering constraints and present our current work on improving performance through BERT-embedding based contextual similarity.
https://wn.com/Disambiguation_–_Linking_Data_Science_And_Engineering_|_Nlp_Summit_2020
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/
Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/
Watch all NLP Summit 2020 sessions: https://www.nlpsummit.org/
Disambiguation or Entity Linking is the assignment of a knowledge base identifier (Wikidata, Wikipedia) to a named entity. Our goal was to improve an MVP model by adding newly created knowledge while maintaining competitive F1 scores.
Taking an entity linking model from MVP into production in a spaCy-native pipeline architecture posed several data science and engineering challenges, such as hyperparameter estimation and knowledge enhancement, which we addressed by taking advantage of the engineering tools Docker and Kubernetes to semi-automate training as an on-demand job.
We also discuss some of our learnings and process improvements that were needed to strike a balance between data science goals and engineering constraints and present our current work on improving performance through BERT-embedding based contextual similarity.
35:11
093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
- Order: Reorder
- Duration: 35:11
- Uploaded Date: 30 Nov 2022
- views: 524
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities ...
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning, #Visualization, #General, #Advanced
Region: EMEA
Slides: https://dist.neo4j.com/nodes-20202-slides/093%20Keyword%20Disambiguation%20Using%20Transformers%20and%20Clustering%20to%20Build%20Cleaner%20Knowledge%20Graphs%20-%20NODES2022%20EMEA%20Advanced%206%20-%20Federica%20Ventruto%2C%20Alessia%20Melania%20Lonoce.pdf
Visit https://neo4j.com/nodes-2022 learn more at https://neo4j.com/developer/get-started and engage at https://community.neo4j.com
https://wn.com/093_Keyword_Disambiguation_Using_Transformers_And_Clustering_To_Build_Cleaner_Knowledge_Nodes2022
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning, #Visualization, #General, #Advanced
Region: EMEA
Slides: https://dist.neo4j.com/nodes-20202-slides/093%20Keyword%20Disambiguation%20Using%20Transformers%20and%20Clustering%20to%20Build%20Cleaner%20Knowledge%20Graphs%20-%20NODES2022%20EMEA%20Advanced%206%20-%20Federica%20Ventruto%2C%20Alessia%20Melania%20Lonoce.pdf
Visit https://neo4j.com/nodes-2022 learn more at https://neo4j.com/developer/get-started and engage at https://community.neo4j.com
- published: 30 Nov 2022
- views: 524
0:25
chess king sacrifice
- Order: Reorder
- Duration: 0:25
- Uploaded Date: 01 Dec 2020
- views: 38904677
Credits to: Chessbase India
Credits to: Chessbase India
https://wn.com/Chess_King_Sacrifice
Credits to: Chessbase India
- published: 01 Dec 2020
- views: 38904677
4:54
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
- Order: Reorder
- Duration: 4:54
- Uploaded Date: 27 Oct 2022
- views: 1099
Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be i...
Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, appearance estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) to improve albedo estimation and, hence, fairness. Specifically, we create the first facial albedo evaluation benchmark where subjects are balanced in terms of skin color, and measure accuracy using the Individual Typology Angle (ITA) metric. We then address the light/albedo ambiguity by building on a key observation: the image of the full scene –as opposed to a cropped image of the face– contains important information about lighting that can be used for disambiguation. TRUST regresses facial albedo by conditioning on both the face region and a global illumination signal obtained from the scene image. Our experimental results show significant improvement compared to state- of-the-art methods on albedo estimation, both in terms of accuracy and fairness. The evaluation benchmark and code are available for research purposes at https://trust.is.tue.mpg.de.
PDF: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730072.pdf
Project: https://trust.is.tue.mpg.de/index.html
Code: https://github.com/HavenFeng/TRUST
Dataset: https://trust.is.tue.mpg.de/login.php
Reference:
@inproceedings{TRUST:ECCV2022,
title = {Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation},
author = {Feng, Haiwen and Bolkart, Timo and Tesch, Joachim and Black, Michael J. and Abrevaya, Victoria},
booktitle = {European Conference on Computer Vision (ECCV)},
publisher = {Springer International Publishing},
month = oct,
year = {2022},
doi = {},
month_numeric = {10}
}
https://wn.com/Towards_Racially_Unbiased_Skin_Tone_Estimation_Via_Scene_Disambiguation
Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, appearance estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) to improve albedo estimation and, hence, fairness. Specifically, we create the first facial albedo evaluation benchmark where subjects are balanced in terms of skin color, and measure accuracy using the Individual Typology Angle (ITA) metric. We then address the light/albedo ambiguity by building on a key observation: the image of the full scene –as opposed to a cropped image of the face– contains important information about lighting that can be used for disambiguation. TRUST regresses facial albedo by conditioning on both the face region and a global illumination signal obtained from the scene image. Our experimental results show significant improvement compared to state- of-the-art methods on albedo estimation, both in terms of accuracy and fairness. The evaluation benchmark and code are available for research purposes at https://trust.is.tue.mpg.de.
PDF: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730072.pdf
Project: https://trust.is.tue.mpg.de/index.html
Code: https://github.com/HavenFeng/TRUST
Dataset: https://trust.is.tue.mpg.de/login.php
Reference:
@inproceedings{TRUST:ECCV2022,
title = {Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation},
author = {Feng, Haiwen and Bolkart, Timo and Tesch, Joachim and Black, Michael J. and Abrevaya, Victoria},
booktitle = {European Conference on Computer Vision (ECCV)},
publisher = {Springer International Publishing},
month = oct,
year = {2022},
doi = {},
month_numeric = {10}
}
- published: 27 Oct 2022
- views: 1099
2:24:42
ApplyAI Hands-on in NLP: Word Disambiguation and Automatic Summarization
- Order: Reorder
- Duration: 2:24:42
- Uploaded Date: 09 May 2020
- views: 1382
You can find the Google Drive folder with the notebooks here: https://drive.google.com/drive/folders/1paIso1fqasLblXgjvkzOwEns4cO81ipc
You can find the Google Drive folder with the notebooks here: https://drive.google.com/drive/folders/1paIso1fqasLblXgjvkzOwEns4cO81ipc
https://wn.com/Applyai_Hands_On_In_Nlp_Word_Disambiguation_And_Automatic_Summarization
You can find the Google Drive folder with the notebooks here: https://drive.google.com/drive/folders/1paIso1fqasLblXgjvkzOwEns4cO81ipc
- published: 09 May 2020
- views: 1382
36:43
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee | Code BEAM America 2021
- Order: Reorder
- Duration: 36:43
- Uploaded Date: 16 Sep 2022
- views: 400
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanes...
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee - Senior Software Engineer at Interfolio
ABSTRACT
Synonymity and homonymity make name disambiguation difficult. To ease this difficulty, I combined two unmaintained Elixir string comparison libraries and added preprocessing and a double metaphone algorithm. The result is a comprehensive map of scores for pattern identification and machine learning. This talk will address the pre-processing, algorithms, and scoring as well as the strengths and limitations. A live demonstration of scoring will allow us to identify patterns. We end with a discussion of how to gain further benefits from the scores.
OBJECTIVES:
To introduce the problem of name disambiguation and string comparison by looking at two existing string comparison libraries before addressing the process of combining them into a single repository. I hope attendees will leave understanding the problem as well as the strengths, limitations, and possibilities of the new library and how it can be used to address the challenges of name disambiguation.
AUDIENCE:
Beginner to intermediate programmers.
• Timecodes
00:00 - 03:54 - Intro
03:55 - 05:14 - String Comparison Algorithms
05:15 - 09:42 - Akin
09:43 - 13:20 - Axon & Training Data: DBLP
13:21 - 18:09 - NX and Axon
18:10 - 19:36 - What's next?
19:36 - 36:43 - QnA
• Follow us on social:
Twitter: https://twitter.com/CodeBEAMio
LinkedIn: https://www.linkedin.com/company/27159258
• Looking for a unique learning experience?
Attend the next Code Sync conference near you!
See what's coming up at: https://codesync.global
• SUBSCRIBE TO OUR CHANNEL
https://www.youtube.com/channel/UC47eUBNO8KBH_V8AfowOWOw
See what's coming up at: https://codesync.global
https://wn.com/And_Yet_Akin_Name_Disambiguation_In_Elixir_|_Vanessa_Lee_|_Code_Beam_America_2021
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee - Senior Software Engineer at Interfolio
ABSTRACT
Synonymity and homonymity make name disambiguation difficult. To ease this difficulty, I combined two unmaintained Elixir string comparison libraries and added preprocessing and a double metaphone algorithm. The result is a comprehensive map of scores for pattern identification and machine learning. This talk will address the pre-processing, algorithms, and scoring as well as the strengths and limitations. A live demonstration of scoring will allow us to identify patterns. We end with a discussion of how to gain further benefits from the scores.
OBJECTIVES:
To introduce the problem of name disambiguation and string comparison by looking at two existing string comparison libraries before addressing the process of combining them into a single repository. I hope attendees will leave understanding the problem as well as the strengths, limitations, and possibilities of the new library and how it can be used to address the challenges of name disambiguation.
AUDIENCE:
Beginner to intermediate programmers.
• Timecodes
00:00 - 03:54 - Intro
03:55 - 05:14 - String Comparison Algorithms
05:15 - 09:42 - Akin
09:43 - 13:20 - Axon & Training Data: DBLP
13:21 - 18:09 - NX and Axon
18:10 - 19:36 - What's next?
19:36 - 36:43 - QnA
• Follow us on social:
Twitter: https://twitter.com/CodeBEAMio
LinkedIn: https://www.linkedin.com/company/27159258
• Looking for a unique learning experience?
Attend the next Code Sync conference near you!
See what's coming up at: https://codesync.global
• SUBSCRIBE TO OUR CHANNEL
https://www.youtube.com/channel/UC47eUBNO8KBH_V8AfowOWOw
See what's coming up at: https://codesync.global
- published: 16 Sep 2022
- views: 400
2:43
Disambiguation Of Ambiguous Terms.
- Order: Reorder
- Duration: 2:43
- Uploaded Date: 05 Apr 2013
- views: 1059
Ambiguity is tough for computers. It can be tough for humans to disambiguate ambiguous terms, but we tend to be pretty good at it. To be great at it you need co...
Ambiguity is tough for computers. It can be tough for humans to disambiguate ambiguous terms, but we tend to be pretty good at it. To be great at it you need context. That is what Stremor's Liquid helium does. It figures out the context of words and disambiguates them.
https://wn.com/Disambiguation_Of_Ambiguous_Terms.
Ambiguity is tough for computers. It can be tough for humans to disambiguate ambiguous terms, but we tend to be pretty good at it. To be great at it you need context. That is what Stremor's Liquid helium does. It figures out the context of words and disambiguates them.
- published: 05 Apr 2013
- views: 1059
6:59
Word Sense Disambiguation
- Order: Reorder
- Duration: 6:59
- Uploaded Date: 28 Dec 2020
- views: 10117
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sens...
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sense%20Disambiguation.pdf
Twitter: @NatalieParde
https://wn.com/Word_Sense_Disambiguation
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sense%20Disambiguation.pdf
Twitter: @NatalieParde
- published: 28 Dec 2020
- views: 10117
40:56
disambiguation
- Order: Reorder
- Duration: 40:56
- Uploaded Date: 04 Oct 2019
- views: 78
- published: 04 Oct 2019
- views: 78
3:16
Underoath - Disambiguation - A Divine Eradication
- Order: Reorder
- Duration: 3:16
- Uploaded Date: 02 Nov 2010
- views: 96855
All music rights go to Underoath, Solid State Records, and Tooth and Nail Records.
Album line up:
1. In Division
2. Catch Myself Catching Myself
3. Paper Lu...
All music rights go to Underoath, Solid State Records, and Tooth and Nail Records.
Album line up:
1. In Division
2. Catch Myself Catching Myself
3. Paper Lung
4. Illuminator
5. Driftwood
6. A Divine Eradication
7. Who Will Guard the Guardians
8. Reversal
9. Vacant Mouth
10. My Deteriorating Incline
11. In Completion
https://wn.com/Underoath_Disambiguation_A_Divine_Eradication
All music rights go to Underoath, Solid State Records, and Tooth and Nail Records.
Album line up:
1. In Division
2. Catch Myself Catching Myself
3. Paper Lung
4. Illuminator
5. Driftwood
6. A Divine Eradication
7. Who Will Guard the Guardians
8. Reversal
9. Vacant Mouth
10. My Deteriorating Incline
11. In Completion
- published: 02 Nov 2010
- views: 96855
PLAYLIST TIME:
PLAYLIST TIME:
29:09
Disambiguation – Linking Data Science and Engineering | NLP Summit 2020
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-t...
published: 07 Jan 2021
Disambiguation – Linking Data Science and Engineering | NLP Summit 2020
Disambiguation – Linking Data Science and Engineering | NLP Summit 2020
- Report rights infringement
- published: 07 Jan 2021
- views: 553
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/
Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/
Watch all NLP Summit 2020 sessions: https://www.nlpsummit.org/
Disambiguation or Entity Linking is the assignment of a knowledge base identifier (Wikidata, Wikipedia) to a named entity. Our goal was to improve an MVP model by adding newly created knowledge while maintaining competitive F1 scores.
Taking an entity linking model from MVP into production in a spaCy-native pipeline architecture posed several data science and engineering challenges, such as hyperparameter estimation and knowledge enhancement, which we addressed by taking advantage of the engineering tools Docker and Kubernetes to semi-automate training as an on-demand job.
We also discuss some of our learnings and process improvements that were needed to strike a balance between data science goals and engineering constraints and present our current work on improving performance through BERT-embedding based contextual similarity.
35:11
093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
Natural language processing is an indispensable toolkit to build knowledge graphs from uns...
published: 30 Nov 2022
093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
- Report rights infringement
- published: 30 Nov 2022
- views: 524
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning, #Visualization, #General, #Advanced
Region: EMEA
Slides: https://dist.neo4j.com/nodes-20202-slides/093%20Keyword%20Disambiguation%20Using%20Transformers%20and%20Clustering%20to%20Build%20Cleaner%20Knowledge%20Graphs%20-%20NODES2022%20EMEA%20Advanced%206%20-%20Federica%20Ventruto%2C%20Alessia%20Melania%20Lonoce.pdf
Visit https://neo4j.com/nodes-2022 learn more at https://neo4j.com/developer/get-started and engage at https://community.neo4j.com
0:25
chess king sacrifice
Credits to: Chessbase India
published: 01 Dec 2020
chess king sacrifice
chess king sacrifice
- Report rights infringement
- published: 01 Dec 2020
- views: 38904677
Credits to: Chessbase India
4:54
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
Virtual facial avatars will play an increasingly important role in immersive communication...
published: 27 Oct 2022
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation
- Report rights infringement
- published: 27 Oct 2022
- views: 1099
Virtual facial avatars will play an increasingly important role in immersive communication, games and the metaverse, and it is therefore critical that they be inclusive. This requires accurate recovery of the albedo, regardless of age, sex, or ethnicity. While significant progress has been made on estimating 3D facial geometry, appearance estimation has received less attention. The task is fundamentally ambiguous because the observed color is a function of albedo and lighting, both of which are unknown. We find that current methods are biased towards light skin tones due to (1) strongly biased priors that prefer lighter pigmentation and (2) algorithmic solutions that disregard the light/albedo ambiguity. To address this, we propose a new evaluation dataset (FAIR) and an algorithm (TRUST) to improve albedo estimation and, hence, fairness. Specifically, we create the first facial albedo evaluation benchmark where subjects are balanced in terms of skin color, and measure accuracy using the Individual Typology Angle (ITA) metric. We then address the light/albedo ambiguity by building on a key observation: the image of the full scene –as opposed to a cropped image of the face– contains important information about lighting that can be used for disambiguation. TRUST regresses facial albedo by conditioning on both the face region and a global illumination signal obtained from the scene image. Our experimental results show significant improvement compared to state- of-the-art methods on albedo estimation, both in terms of accuracy and fairness. The evaluation benchmark and code are available for research purposes at https://trust.is.tue.mpg.de.
PDF: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136730072.pdf
Project: https://trust.is.tue.mpg.de/index.html
Code: https://github.com/HavenFeng/TRUST
Dataset: https://trust.is.tue.mpg.de/login.php
Reference:
@inproceedings{TRUST:ECCV2022,
title = {Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation},
author = {Feng, Haiwen and Bolkart, Timo and Tesch, Joachim and Black, Michael J. and Abrevaya, Victoria},
booktitle = {European Conference on Computer Vision (ECCV)},
publisher = {Springer International Publishing},
month = oct,
year = {2022},
doi = {},
month_numeric = {10}
}
2:24:42
ApplyAI Hands-on in NLP: Word Disambiguation and Automatic Summarization
You can find the Google Drive folder with the notebooks here: https://drive.google.com/dri...
published: 09 May 2020
ApplyAI Hands-on in NLP: Word Disambiguation and Automatic Summarization
ApplyAI Hands-on in NLP: Word Disambiguation and Automatic Summarization
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- published: 09 May 2020
- views: 1382
You can find the Google Drive folder with the notebooks here: https://drive.google.com/drive/folders/1paIso1fqasLblXgjvkzOwEns4cO81ipc
36:43
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee | Code BEAM America 2021
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/c...
published: 16 Sep 2022
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee | Code BEAM America 2021
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee | Code BEAM America 2021
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- published: 16 Sep 2022
- views: 400
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee - Senior Software Engineer at Interfolio
ABSTRACT
Synonymity and homonymity make name disambiguation difficult. To ease this difficulty, I combined two unmaintained Elixir string comparison libraries and added preprocessing and a double metaphone algorithm. The result is a comprehensive map of scores for pattern identification and machine learning. This talk will address the pre-processing, algorithms, and scoring as well as the strengths and limitations. A live demonstration of scoring will allow us to identify patterns. We end with a discussion of how to gain further benefits from the scores.
OBJECTIVES:
To introduce the problem of name disambiguation and string comparison by looking at two existing string comparison libraries before addressing the process of combining them into a single repository. I hope attendees will leave understanding the problem as well as the strengths, limitations, and possibilities of the new library and how it can be used to address the challenges of name disambiguation.
AUDIENCE:
Beginner to intermediate programmers.
• Timecodes
00:00 - 03:54 - Intro
03:55 - 05:14 - String Comparison Algorithms
05:15 - 09:42 - Akin
09:43 - 13:20 - Axon & Training Data: DBLP
13:21 - 18:09 - NX and Axon
18:10 - 19:36 - What's next?
19:36 - 36:43 - QnA
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2:43
Disambiguation Of Ambiguous Terms.
Ambiguity is tough for computers. It can be tough for humans to disambiguate ambiguous ter...
published: 05 Apr 2013
Disambiguation Of Ambiguous Terms.
Disambiguation Of Ambiguous Terms.
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- published: 05 Apr 2013
- views: 1059
Ambiguity is tough for computers. It can be tough for humans to disambiguate ambiguous terms, but we tend to be pretty good at it. To be great at it you need context. That is what Stremor's Liquid helium does. It figures out the context of words and disambiguates them.
6:59
Word Sense Disambiguation
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Sl...
published: 28 Dec 2020
Word Sense Disambiguation
Word Sense Disambiguation
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- published: 28 Dec 2020
- views: 10117
Material based on Jurafsky and Martin (2019): https://web.stanford.edu/~jurafsky/slp3/
Slides: http://www.natalieparde.com/teaching/cs_421_fall2020/Word%20Sense%20Disambiguation.pdf
Twitter: @NatalieParde
3:16
Underoath - Disambiguation - A Divine Eradication
All music rights go to Underoath, Solid State Records, and Tooth and Nail Records.
Album ...
published: 02 Nov 2010
Underoath - Disambiguation - A Divine Eradication
Underoath - Disambiguation - A Divine Eradication
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- published: 02 Nov 2010
- views: 96855
All music rights go to Underoath, Solid State Records, and Tooth and Nail Records.
Album line up:
1. In Division
2. Catch Myself Catching Myself
3. Paper Lung
4. Illuminator
5. Driftwood
6. A Divine Eradication
7. Who Will Guard the Guardians
8. Reversal
9. Vacant Mouth
10. My Deteriorating Incline
11. In Completion
So Close (disambiguation)
So Close is a Hong Kong action film.
So Close may also refer to:
See also
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/So_Close_(disambiguation)
Disambiguation – Linking Data Science and Engineer...
093 Keyword Disambiguation Using Transformers and ...
chess king sacrifice...
Towards Racially Unbiased Skin Tone Estimation via...
ApplyAI Hands-on in NLP: Word Disambiguation and ...
And Yet Akin: Name Disambiguation in Elixir | Vane...
Disambiguation Of Ambiguous Terms....
Word Sense Disambiguation...
disambiguation...
Underoath - Disambiguation - A Divine Eradication...
-
by Libbie Schrader
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by Jon Mclaughlin
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by Julius Isidro
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by Daryl Hall & John Oates
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by Oceans Of Sadness
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by Lee Greenwood
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by Hillsong United
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by Bonnie Raitt
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by Not By Choice
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by Such Gold
-
by Evanescence
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by Jennette Mccurdy
-
by Waiting For Autumn
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by Chaka Khan
-
by Dina Carroll
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by Alice In Chains
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by Mary Mary
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by Better Off
-
by Rachel Proctor
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by Diana Ross
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by Jaded Monkeys
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by Darling Buds
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by Brook Benton
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by Hall & Oates
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by Flying Pickets
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by Jill Paquette
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by Deon Kipping
So Close
by: Libbie SchraderYou sit here beside me
At the corner of my eye
If you move just a little
We'll be touching that line
All that you've given
All that you've done
Could still be forgiven
If you were found out
And you bring me here so close
To the single thing that I want the most
But it's just a ghost
It won't be given
You play me so sweet
Does she know that you come to me
While her light is burning late for you?
The words that you've spoken
Only to me
Still haven't broken
That promise that you keep
But you bring me here so close
To the single thing that I want the most
And it's just a ghost
It won't be given
You play me so sweet
Does she know that you come to me
While her light is burning late for you?
You play me so sweet
And I start to forget that she's
Got her light burning late for you
So you lead me here this far
But you'll be recognized now for what you are
Close, but still too far
For what I'm wishing
Oh, you play me so sweet
Does she know that you come to me
While her light is burning late for you?
You play me oh so sweet
I don't know what you want from me