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Disambiguation of Data Mesh, Fabric, Centric, Driven, and Everything!
Dan DeMers, CEO and Co-Founder of Cinchy, and Dave McComb, President of Semantic Arts and Best-Selling Author to break down the ambiguity of six data systems and products you’ve likely heard of but want to understand the difference
published: 19 Mar 2021
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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
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Word order and disambiguation in Pangasinan
Joey Lim and Michael Yoshitaka Erlewine, presented at SICOGG 22, August 2020
Pangasinan (Austronesian; northern Philippines) has case marking and some agreement and generally free postverbal word order. However, in a particular, limited configuration where case and agreement fail to disambiguate between core arguments, their word order becomes rigid. We argue that these facts are explained by a feature-driven approach to scrambling, where scrambling probes can only target morphosyntactic features and not A'-features. This work shows that the well-known trade-off between word order rigidity and overt case/agreement is not merely a typological tendency, but can also be observed in an individual's synchronic grammar.
published: 09 Aug 2020
-
【UTAUカバー】マインドブランド (Mind Brand) 【スズ -XCROSS-】
sc post: https://soundcloud.com/akemiyuyu/utau-mind-brand-xcross
==
hi
curretn mood
bye
oh i also drew a little cover art thingy but ended up not using it for this video lmao just in case
http://kemiyukishi.deviantart.com/art/mindfuck-570956142
==
UST by マコロン (thanks!)
Harmonies, tuning, adlib, mix, Suzu by me
Subs by FreedomT1
Original by MARETU
(yes im kinda maretu-obsessed rn help)
published: 14 Nov 2015
-
Pandelis Karayorgis Trio, Disambiguation
Pandelis Karayorgis Trio
Pandelis Karayorgis, piano
Nate McBride, bass
Randy Peterson, drums
December 3, 2016
Third Life Studio, Somerville, MA
published: 06 Dec 2016
-
emf003: Induction Disambiguation; NE model for light
Distinti disambiguates the results of the Quad Loop Experiment to show New Induction is the best fit for the phenomenon of induction
Please support Ethereal Mechanics!
Our Patreon Site:
https://www.patreon.com/Etherealmechanics
Join our Forum:
https://www.etherealmechanics.info
published: 29 Feb 2016
-
Relational machine learning author disambiguation | J. Millan, E. Bastrakova, R. Ledesma
Relational machine learning author disambiguation | Конференция: AINL FRUCT: Artificial Intelligence and Natural Language Conference 2016 | Лектор: Jose Millan, Ekaterina Bastrakova, Rodney Ledesma | Организатор: AINL FRUCT Conference
Смотрите это видео на Лекториуме: https://www.lektorium.tv/lecture/29480
Подписывайтесь на канал: https://www.lektorium.tv/ZJA
Следите за новостями:
https://vk.com/openlektorium
https://www.facebook.com/openlektorium
published: 12 Dec 2016
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Computational Semantics Evaluation: The Origins of Senseval and Evolution of SemEval
About the event:
Senseval and SemEval datasets have come to be the de facto gold standards for much of the work in computational semantics. The events that spawned these datasets have enabled comparison of systems, fostered a large community of researchers and spawned interest in a wide variety of semantic tasks. In my talk, I will discuss the origins of these events in the evaluation of word sense disambiguation systems and I will describe a sample of the tasks that have arisen as SemEval has evolved. These `evaluation exercises' have brought many benefits to the field in terms of algorithms and data as well as new players and tasks. Nevertheless, various issues have arisen from the very inception of Senseval and need consideration when interpreting results and designing tasks. One ...
published: 16 Jul 2018
-
Partial Label Learning via Feature-Aware Disambiguation
Author:
Xiangnan Kong, Department of Computer Science, Worcester Polytechnic Institute
Abstract:
Partial label learning deals with the problem where each training example is represented by a feature vector while associated with a set of candidate labels, among which only one label is valid. To learn from such ambiguous labeling information, the key is to try to disambiguate the candidate label sets of partial label training examples. Existing disambiguation strategies work by either identifying the ground-truth label iteratively or treating each candidate label equally. Nonetheless, the disambiguation process is generally conducted by focusing on manipulating the label space, and thus ignores making full use of potentially useful information from the feature space. In this paper, a nove...
published: 10 Oct 2016
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03 Invited Talk – Prof. Galia Angelova Tag Sense Disambiguation ...
Invited Talk – Prof. D.Sc. Galia Angelova (Institute of Information and Communication Technologies): Tag Sense Disambiguation in Large Image Collections: Is It Possible?
published: 07 Jul 2020
1:01:29
Disambiguation of Data Mesh, Fabric, Centric, Driven, and Everything!
Dan DeMers, CEO and Co-Founder of Cinchy, and Dave McComb, President of Semantic Arts and Best-Selling Author to break down the ambiguity of six data systems an...
Dan DeMers, CEO and Co-Founder of Cinchy, and Dave McComb, President of Semantic Arts and Best-Selling Author to break down the ambiguity of six data systems and products you’ve likely heard of but want to understand the difference
https://wn.com/Disambiguation_Of_Data_Mesh,_Fabric,_Centric,_Driven,_And_Everything
Dan DeMers, CEO and Co-Founder of Cinchy, and Dave McComb, President of Semantic Arts and Best-Selling Author to break down the ambiguity of six data systems and products you’ve likely heard of but want to understand the difference
- published: 19 Mar 2021
- views: 914
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/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: 1052
21:08
Word order and disambiguation in Pangasinan
Joey Lim and Michael Yoshitaka Erlewine, presented at SICOGG 22, August 2020
Pangasinan (Austronesian; northern Philippines) has case marking and some agreem...
Joey Lim and Michael Yoshitaka Erlewine, presented at SICOGG 22, August 2020
Pangasinan (Austronesian; northern Philippines) has case marking and some agreement and generally free postverbal word order. However, in a particular, limited configuration where case and agreement fail to disambiguate between core arguments, their word order becomes rigid. We argue that these facts are explained by a feature-driven approach to scrambling, where scrambling probes can only target morphosyntactic features and not A'-features. This work shows that the well-known trade-off between word order rigidity and overt case/agreement is not merely a typological tendency, but can also be observed in an individual's synchronic grammar.
https://wn.com/Word_Order_And_Disambiguation_In_Pangasinan
Joey Lim and Michael Yoshitaka Erlewine, presented at SICOGG 22, August 2020
Pangasinan (Austronesian; northern Philippines) has case marking and some agreement and generally free postverbal word order. However, in a particular, limited configuration where case and agreement fail to disambiguate between core arguments, their word order becomes rigid. We argue that these facts are explained by a feature-driven approach to scrambling, where scrambling probes can only target morphosyntactic features and not A'-features. This work shows that the well-known trade-off between word order rigidity and overt case/agreement is not merely a typological tendency, but can also be observed in an individual's synchronic grammar.
- published: 09 Aug 2020
- views: 147
4:20
【UTAUカバー】マインドブランド (Mind Brand) 【スズ -XCROSS-】
sc post: https://soundcloud.com/akemiyuyu/utau-mind-brand-xcross
==
hi
curretn mood
bye
oh i also drew a little cover art thingy but ended up not using it ...
sc post: https://soundcloud.com/akemiyuyu/utau-mind-brand-xcross
==
hi
curretn mood
bye
oh i also drew a little cover art thingy but ended up not using it for this video lmao just in case
http://kemiyukishi.deviantart.com/art/mindfuck-570956142
==
UST by マコロン (thanks!)
Harmonies, tuning, adlib, mix, Suzu by me
Subs by FreedomT1
Original by MARETU
(yes im kinda maretu-obsessed rn help)
https://wn.com/【Utauカバー】マインドブランド_(Mind_Brand)_【スズ_Xcross_】
sc post: https://soundcloud.com/akemiyuyu/utau-mind-brand-xcross
==
hi
curretn mood
bye
oh i also drew a little cover art thingy but ended up not using it for this video lmao just in case
http://kemiyukishi.deviantart.com/art/mindfuck-570956142
==
UST by マコロン (thanks!)
Harmonies, tuning, adlib, mix, Suzu by me
Subs by FreedomT1
Original by MARETU
(yes im kinda maretu-obsessed rn help)
- published: 14 Nov 2015
- views: 360587
10:07
Pandelis Karayorgis Trio, Disambiguation
Pandelis Karayorgis Trio
Pandelis Karayorgis, piano
Nate McBride, bass
Randy Peterson, drums
December 3, 2016
Third Life Studio, Somerville, MA
Pandelis Karayorgis Trio
Pandelis Karayorgis, piano
Nate McBride, bass
Randy Peterson, drums
December 3, 2016
Third Life Studio, Somerville, MA
https://wn.com/Pandelis_Karayorgis_Trio,_Disambiguation
Pandelis Karayorgis Trio
Pandelis Karayorgis, piano
Nate McBride, bass
Randy Peterson, drums
December 3, 2016
Third Life Studio, Somerville, MA
- published: 06 Dec 2016
- views: 1115
25:10
emf003: Induction Disambiguation; NE model for light
Distinti disambiguates the results of the Quad Loop Experiment to show New Induction is the best fit for the phenomenon of induction
Please support Ethereal Me...
Distinti disambiguates the results of the Quad Loop Experiment to show New Induction is the best fit for the phenomenon of induction
Please support Ethereal Mechanics!
Our Patreon Site:
https://www.patreon.com/Etherealmechanics
Join our Forum:
https://www.etherealmechanics.info
https://wn.com/Emf003_Induction_Disambiguation_Ne_Model_For_Light
Distinti disambiguates the results of the Quad Loop Experiment to show New Induction is the best fit for the phenomenon of induction
Please support Ethereal Mechanics!
Our Patreon Site:
https://www.patreon.com/Etherealmechanics
Join our Forum:
https://www.etherealmechanics.info
- published: 29 Feb 2016
- views: 1447
16:13
Relational machine learning author disambiguation | J. Millan, E. Bastrakova, R. Ledesma
Relational machine learning author disambiguation | Конференция: AINL FRUCT: Artificial Intelligence and Natural Language Conference 2016 | Лектор: Jose Millan,...
Relational machine learning author disambiguation | Конференция: AINL FRUCT: Artificial Intelligence and Natural Language Conference 2016 | Лектор: Jose Millan, Ekaterina Bastrakova, Rodney Ledesma | Организатор: AINL FRUCT Conference
Смотрите это видео на Лекториуме: https://www.lektorium.tv/lecture/29480
Подписывайтесь на канал: https://www.lektorium.tv/ZJA
Следите за новостями:
https://vk.com/openlektorium
https://www.facebook.com/openlektorium
https://wn.com/Relational_Machine_Learning_Author_Disambiguation_|_J._Millan,_E._Bastrakova,_R._Ledesma
Relational machine learning author disambiguation | Конференция: AINL FRUCT: Artificial Intelligence and Natural Language Conference 2016 | Лектор: Jose Millan, Ekaterina Bastrakova, Rodney Ledesma | Организатор: AINL FRUCT Conference
Смотрите это видео на Лекториуме: https://www.lektorium.tv/lecture/29480
Подписывайтесь на канал: https://www.lektorium.tv/ZJA
Следите за новостями:
https://vk.com/openlektorium
https://www.facebook.com/openlektorium
- published: 12 Dec 2016
- views: 369
56:27
Computational Semantics Evaluation: The Origins of Senseval and Evolution of SemEval
About the event:
Senseval and SemEval datasets have come to be the de facto gold standards for much of the work in computational semantics. The events that spa...
About the event:
Senseval and SemEval datasets have come to be the de facto gold standards for much of the work in computational semantics. The events that spawned these datasets have enabled comparison of systems, fostered a large community of researchers and spawned interest in a wide variety of semantic tasks. In my talk, I will discuss the origins of these events in the evaluation of word sense disambiguation systems and I will describe a sample of the tasks that have arisen as SemEval has evolved. These `evaluation exercises' have brought many benefits to the field in terms of algorithms and data as well as new players and tasks. Nevertheless, various issues have arisen from the very inception of Senseval and need consideration when interpreting results and designing tasks. One issue that I will particularly discuss is representation of semantic phenomena and the pros and cons of tasks that are representation independent.
Speaker bio:
Dr. Diana McCarthy is an affiliated lecturer at the University of Cambridge, UK. She has been active in the field of computational linguistics for over 20 years and specialises in the field of computational lexical semantics. Along with various collaborators, she won best paper awards for her work on automatic detection of sense predominance and for compositionality modelling of compound nouns with distributional semantics. Prior to her affiliation at Cambridge, she held a Dorothy Hodgkin Research Fellowship from the UK Royal Society at the University of Sussex from 2005 until 2009. From 2009 until 2012 she worked as a director and computational linguist for the research-led company 'Lexical Computing Ltd' whose main product is the Sketch Engine. Her work includes evaluation of computational models of lexical semantics and she has been involved in the Senseval (now SemEval) community since the first event in 1998 as both participant and task organiser.
#TuringSeminars
https://wn.com/Computational_Semantics_Evaluation_The_Origins_Of_Senseval_And_Evolution_Of_Semeval
About the event:
Senseval and SemEval datasets have come to be the de facto gold standards for much of the work in computational semantics. The events that spawned these datasets have enabled comparison of systems, fostered a large community of researchers and spawned interest in a wide variety of semantic tasks. In my talk, I will discuss the origins of these events in the evaluation of word sense disambiguation systems and I will describe a sample of the tasks that have arisen as SemEval has evolved. These `evaluation exercises' have brought many benefits to the field in terms of algorithms and data as well as new players and tasks. Nevertheless, various issues have arisen from the very inception of Senseval and need consideration when interpreting results and designing tasks. One issue that I will particularly discuss is representation of semantic phenomena and the pros and cons of tasks that are representation independent.
Speaker bio:
Dr. Diana McCarthy is an affiliated lecturer at the University of Cambridge, UK. She has been active in the field of computational linguistics for over 20 years and specialises in the field of computational lexical semantics. Along with various collaborators, she won best paper awards for her work on automatic detection of sense predominance and for compositionality modelling of compound nouns with distributional semantics. Prior to her affiliation at Cambridge, she held a Dorothy Hodgkin Research Fellowship from the UK Royal Society at the University of Sussex from 2005 until 2009. From 2009 until 2012 she worked as a director and computational linguist for the research-led company 'Lexical Computing Ltd' whose main product is the Sketch Engine. Her work includes evaluation of computational models of lexical semantics and she has been involved in the Senseval (now SemEval) community since the first event in 1998 as both participant and task organiser.
#TuringSeminars
- published: 16 Jul 2018
- views: 727
18:58
Partial Label Learning via Feature-Aware Disambiguation
Author:
Xiangnan Kong, Department of Computer Science, Worcester Polytechnic Institute
Abstract:
Partial label learning deals with the problem where each tra...
Author:
Xiangnan Kong, Department of Computer Science, Worcester Polytechnic Institute
Abstract:
Partial label learning deals with the problem where each training example is represented by a feature vector while associated with a set of candidate labels, among which only one label is valid. To learn from such ambiguous labeling information, the key is to try to disambiguate the candidate label sets of partial label training examples. Existing disambiguation strategies work by either identifying the ground-truth label iteratively or treating each candidate label equally. Nonetheless, the disambiguation process is generally conducted by focusing on manipulating the label space, and thus ignores making full use of potentially useful information from the feature space. In this paper, a novel two-stage approach is proposed to learning from partial label examples based on feature-aware disambiguation. In the first stage, the manifold structure of feature space is utilized to generate normalized labeling confidences over candidate label set. In the second stage, the predictive model is learned by performing regularized multi-output regression over the generated labeling confidences. Extensive experiments on artificial as well as real-world partial label data sets clearly validate the superiority of the proposed feature-aware disambiguation approach.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/
https://wn.com/Partial_Label_Learning_Via_Feature_Aware_Disambiguation
Author:
Xiangnan Kong, Department of Computer Science, Worcester Polytechnic Institute
Abstract:
Partial label learning deals with the problem where each training example is represented by a feature vector while associated with a set of candidate labels, among which only one label is valid. To learn from such ambiguous labeling information, the key is to try to disambiguate the candidate label sets of partial label training examples. Existing disambiguation strategies work by either identifying the ground-truth label iteratively or treating each candidate label equally. Nonetheless, the disambiguation process is generally conducted by focusing on manipulating the label space, and thus ignores making full use of potentially useful information from the feature space. In this paper, a novel two-stage approach is proposed to learning from partial label examples based on feature-aware disambiguation. In the first stage, the manifold structure of feature space is utilized to generate normalized labeling confidences over candidate label set. In the second stage, the predictive model is learned by performing regularized multi-output regression over the generated labeling confidences. Extensive experiments on artificial as well as real-world partial label data sets clearly validate the superiority of the proposed feature-aware disambiguation approach.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/
- published: 10 Oct 2016
- views: 241
54:43
03 Invited Talk – Prof. Galia Angelova Tag Sense Disambiguation ...
Invited Talk – Prof. D.Sc. Galia Angelova (Institute of Information and Communication Technologies): Tag Sense Disambiguation in Large Image Collections: Is It ...
Invited Talk – Prof. D.Sc. Galia Angelova (Institute of Information and Communication Technologies): Tag Sense Disambiguation in Large Image Collections: Is It Possible?
https://wn.com/03_Invited_Talk_–_Prof._Galia_Angelova_Tag_Sense_Disambiguation_...
Invited Talk – Prof. D.Sc. Galia Angelova (Institute of Information and Communication Technologies): Tag Sense Disambiguation in Large Image Collections: Is It Possible?
- published: 07 Jul 2020
- views: 12