- published: 03 Jul 2012
- views: 3088
-
remove the playlistNon-state (disambiguation)
- remove the playlistNon-state (disambiguation)
Non-state (disambiguation)
Non-state describes a player on scene where states are major players.
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Non-state_(disambiguation)
Non-state actor
Non-state actors (NSA) are entities that participate or act in international relations. They are organizations with sufficient power to influence and cause a change even though they do not belong to any established institution of a state.
The admission of non-state actors into international relations theory conflicts with the assumptions of realism and other black box theories of international relations, which argue that interactions between states are the main relationships of interest in studying international events.
Types
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Non-state_actor
Podcasts:
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Mod-01 Lec-31 Wordnet; Metonymy and Word Sense Disambiguation
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
published: 03 Jul 2012 -
KGC 2022: KG-Based Approach to Named Entity Disambiguation for Healthcare Applications — GraphAware
Senior Data Scientist at GraphAware, Giuseppe Futia, shows how to leverage a Named Entity Disambiguation (NED) system to disambiguate named entities in the healthcare domain and combine multiple knowledge graphs and ontologies in a single valuable source of truth. The approach incorporates node embeddings into the NED model, employing the KG structure for the training process. The tool can support different healthcare applications, including literature search and retrieval, clinical decision-making, relational knowledge findings, chatbots for health assistance, and recommendation tools for patients and medical practitioners. Giuseppe Futia holds a Ph.D. in Computer Engineering from the Politecnico di Torino, where he explored Graph Representation Learning techniques to support the auto...
published: 03 Nov 2022 -
[PLDI24] Static Analysis for Checking the Disambiguation Robustness of Regular Expressions
Static Analysis for Checking the Disambiguation Robustness of Regular Expressions (Video, PLDI 2024) Konstantinos Mamouras, Alexis Le Glaunec, Wu Angela Li, and Agnishom Chattopadhyay (Rice University, USA; Rice University, USA; Rice University, USA; Rice University, USA) Abstract: Regular expressions are commonly used for finding and extracting matches from sequence data. Due to the inherent ambiguity of regular expressions, a disambiguation policy must be considered for the match extraction problem, in order to uniquely determine the desired match out of the possibly many matches. The most common disambiguation policies are the POSIX policy and the greedy (PCRE) policy. The POSIX policy chooses the longest match out of the leftmost ones. The greedy policy chooses a leftmost match and fu...
published: 23 Jul 2024 -
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 -
Disambiguation, In-Jokes, and Name Collisions: What You Need to Know When Naming a Python Project
Thursday Bram https://2018.northbaypython.org/schedule/presentation/15/ This talk covers key issues Python programmers run into when naming new projects. We'll go over the following: * Commonly used naming schemas in the Python community * Current and past project names (including those that many newcomers to Python struggle with) * Techniques to avoid similar confusion in the future (covering both name selection and documentation) We'll even talk about Monty Python and its long-term impact on the Python programming language. A Python conference north of the Golden Gate North Bay Python is a single-track conference with a carefully curated set of talks representing the diverse Python community and their different areas of interest. If a topic is less to your interest, or...
published: 16 Nov 2018 -
IWC 2020: Safety and Completeness of Disambiguation corresponds to Termination ... (Eelco Visser)
Talk 11 Full title: Safety and Completeness of Disambiguation corresponds to Termination and Confluence of Reordering
published: 05 Jul 2020 -
How to perform disambiguation of words with WordStat - Content Analysis and Text Mining Software
Learn how to perform disambiguation with WordStat using phrases or rules. To learn more about WordStat: https://provalisresearch.com/products/content-analysis-software/ #textmining #contentanalysis #NLP
published: 11 Sep 2017 -
Adjusting sense representations for knowledge-based word sense disambiguation
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany) Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word carries in a particular context – is a core research problem in computational linguistics. Though it has long been recognized that supervised (i.e., machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and text domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, whi...
published: 31 May 2017 -
Mod-01 Lec-30 Wordnet and Word Sense Disambiguation(contd...)
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
published: 03 Jul 2012 -
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (Research Paper Walkthrough)
#bert #wsd #wordnet This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using the Gloss knowledge. They show state-of-art results on benchmark datasets. ⏩ Abstract: Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a...
published: 07 Apr 2021
developed with YouTube
49:16
Mod-01 Lec-31 Wordnet; Metonymy and Word Sense Disambiguation
- Order: Reorder
- Duration: 49:16
- Uploaded Date: 03 Jul 2012
- views: 3088
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel...
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
https://wn.com/Mod_01_Lec_31_Wordnet_Metonymy_And_Word_Sense_Disambiguation
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
24:46
KGC 2022: KG-Based Approach to Named Entity Disambiguation for Healthcare Applications — GraphAware
- Order: Reorder
- Duration: 24:46
- Uploaded Date: 03 Nov 2022
- views: 393
Senior Data Scientist at GraphAware, Giuseppe Futia, shows how to leverage a Named Entity Disambiguation (NED) system to disambiguate named entities in the heal...
Senior Data Scientist at GraphAware, Giuseppe Futia, shows how to leverage a Named Entity Disambiguation (NED) system to disambiguate named entities in the healthcare domain and combine multiple knowledge graphs and ontologies in a single valuable source of truth.
The approach incorporates node embeddings into the NED model, employing the KG structure for the training process.
The tool can support different healthcare applications, including literature search and retrieval, clinical decision-making, relational knowledge findings, chatbots for health assistance, and recommendation tools for patients and medical practitioners.
Giuseppe Futia holds a Ph.D. in Computer Engineering from the Politecnico di Torino, where he explored Graph Representation Learning techniques to support the automatic building of Knowledge Graphs.
The 5 key takeaways:
1. The components and requirements of the Intelligent Advisory Systems (IAS).
2. How they use Hume, the Neo4j-backed no-code knowledge graph ecosystem.
3. Delving into diabetes real-life use cases and linking to the Unified Medical Language System.
4. How GraphAware utilizes ontology-based enrichment for their knowledge graph-based approach.
5. The cooperation of NED candidates selections and NED candidates ranking.
#biotechnology #lifescience #technology
https://wn.com/Kgc_2022_Kg_Based_Approach_To_Named_Entity_Disambiguation_For_Healthcare_Applications_—_Graphaware
Senior Data Scientist at GraphAware, Giuseppe Futia, shows how to leverage a Named Entity Disambiguation (NED) system to disambiguate named entities in the healthcare domain and combine multiple knowledge graphs and ontologies in a single valuable source of truth.
The approach incorporates node embeddings into the NED model, employing the KG structure for the training process.
The tool can support different healthcare applications, including literature search and retrieval, clinical decision-making, relational knowledge findings, chatbots for health assistance, and recommendation tools for patients and medical practitioners.
Giuseppe Futia holds a Ph.D. in Computer Engineering from the Politecnico di Torino, where he explored Graph Representation Learning techniques to support the automatic building of Knowledge Graphs.
The 5 key takeaways:
1. The components and requirements of the Intelligent Advisory Systems (IAS).
2. How they use Hume, the Neo4j-backed no-code knowledge graph ecosystem.
3. Delving into diabetes real-life use cases and linking to the Unified Medical Language System.
4. How GraphAware utilizes ontology-based enrichment for their knowledge graph-based approach.
5. The cooperation of NED candidates selections and NED candidates ranking.
#biotechnology #lifescience #technology
- published: 03 Nov 2022
- views: 393
22:12
[PLDI24] Static Analysis for Checking the Disambiguation Robustness of Regular Expressions
- Order: Reorder
- Duration: 22:12
- Uploaded Date: 23 Jul 2024
- views: 154
Static Analysis for Checking the Disambiguation Robustness of Regular Expressions (Video, PLDI 2024)
Konstantinos Mamouras, Alexis Le Glaunec, Wu Angela Li, and...
Static Analysis for Checking the Disambiguation Robustness of Regular Expressions (Video, PLDI 2024)
Konstantinos Mamouras, Alexis Le Glaunec, Wu Angela Li, and Agnishom Chattopadhyay
(Rice University, USA; Rice University, USA; Rice University, USA; Rice University, USA)
Abstract: Regular expressions are commonly used for finding and extracting matches from sequence data. Due to the inherent ambiguity of regular expressions, a disambiguation policy must be considered for the match extraction problem, in order to uniquely determine the desired match out of the possibly many matches. The most common disambiguation policies are the POSIX policy and the greedy (PCRE) policy. The POSIX policy chooses the longest match out of the leftmost ones. The greedy policy chooses a leftmost match and further disambiguates using a greedy interpretation of Kleene iteration to match as many times as possible. The choice of disambiguation policy can affect the output of match extraction, which can be an issue for reusing regular expressions across regex engines. In this paper, we introduce and study the notion of disambiguation robustness for regular expressions. A regular expression is robust if its extraction semantics is indifferent to whether the POSIX or greedy disambiguation policy is chosen. This gives rise to a decision problem for regular expressions, which we prove to be PSPACE-complete. We propose a static analysis algorithm for checking the (non-)robustness of regular expressions and two performance optimizations. We have implemented the proposed algorithms and we have shown experimentally that they are practical for analyzing large datasets of regular expressions derived from various application domains.
Article: https://doi.org/10.1145/3656461
ORCID: https://orcid.org/0000-0003-1209-7738, https://orcid.org/0000-0002-5444-5924, https://orcid.org/0000-0002-4523-3401, https://orcid.org/0009-0007-0462-8080
Video Tags: regex, automata, parsing, disambiguation strategy, static analysis, pldi24main-p806-p, doi:10.1145/3656461, orcid:0000-0003-1209-7738, orcid:0000-0002-5444-5924, orcid:0000-0002-4523-3401, orcid:0009-0007-0462-8080
Presentation at the PLDI 2024 conference, June 24–28, 2024, https://pldi24.sigplan.org/
Sponsored by ACM SIGPLAN,
https://wn.com/Pldi24_Static_Analysis_For_Checking_The_Disambiguation_Robustness_Of_Regular_Expressions
Static Analysis for Checking the Disambiguation Robustness of Regular Expressions (Video, PLDI 2024)
Konstantinos Mamouras, Alexis Le Glaunec, Wu Angela Li, and Agnishom Chattopadhyay
(Rice University, USA; Rice University, USA; Rice University, USA; Rice University, USA)
Abstract: Regular expressions are commonly used for finding and extracting matches from sequence data. Due to the inherent ambiguity of regular expressions, a disambiguation policy must be considered for the match extraction problem, in order to uniquely determine the desired match out of the possibly many matches. The most common disambiguation policies are the POSIX policy and the greedy (PCRE) policy. The POSIX policy chooses the longest match out of the leftmost ones. The greedy policy chooses a leftmost match and further disambiguates using a greedy interpretation of Kleene iteration to match as many times as possible. The choice of disambiguation policy can affect the output of match extraction, which can be an issue for reusing regular expressions across regex engines. In this paper, we introduce and study the notion of disambiguation robustness for regular expressions. A regular expression is robust if its extraction semantics is indifferent to whether the POSIX or greedy disambiguation policy is chosen. This gives rise to a decision problem for regular expressions, which we prove to be PSPACE-complete. We propose a static analysis algorithm for checking the (non-)robustness of regular expressions and two performance optimizations. We have implemented the proposed algorithms and we have shown experimentally that they are practical for analyzing large datasets of regular expressions derived from various application domains.
Article: https://doi.org/10.1145/3656461
ORCID: https://orcid.org/0000-0003-1209-7738, https://orcid.org/0000-0002-5444-5924, https://orcid.org/0000-0002-4523-3401, https://orcid.org/0009-0007-0462-8080
Video Tags: regex, automata, parsing, disambiguation strategy, static analysis, pldi24main-p806-p, doi:10.1145/3656461, orcid:0000-0003-1209-7738, orcid:0000-0002-5444-5924, orcid:0000-0002-4523-3401, orcid:0009-0007-0462-8080
Presentation at the PLDI 2024 conference, June 24–28, 2024, https://pldi24.sigplan.org/
Sponsored by ACM SIGPLAN,
- published: 23 Jul 2024
- views: 154
29:09
Disambiguation – Linking Data Science and Engineering | NLP Summit 2020
- Order: Reorder
- Duration: 29:09
- Uploaded Date: 07 Jan 2021
- views: 545
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.
- published: 07 Jan 2021
- views: 545
26:51
Disambiguation, In-Jokes, and Name Collisions: What You Need to Know When Naming a Python Project
- Order: Reorder
- Duration: 26:51
- Uploaded Date: 16 Nov 2018
- views: 175
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers key issues Python programmers run into when naming new projects. We'l...
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers key issues Python programmers run into when naming new projects. We'll go over the following:
* Commonly used naming schemas in the Python community
* Current and past project names (including those that many newcomers to Python struggle with)
* Techniques to avoid similar confusion in the future (covering both name selection and documentation)
We'll even talk about Monty Python and its long-term impact on the Python programming language.
A Python conference north of the Golden Gate
North Bay Python is a single-track conference with a carefully curated set of talks representing the diverse Python community and their different areas of interest.
If a topic is less to your interest, or you've met some people you really want to sit down and chat with, we'll have plenty of areas away from the main theatre to catch up and chat.
Our goal is to keep prices as low as possible. That means we won't be catering lunch. Instead, you can look forward to extra-long lunch breaks you can use to explore all of the great food options around the venue.
https://wn.com/Disambiguation,_In_Jokes,_And_Name_Collisions_What_You_Need_To_Know_When_Naming_A_Python_Project
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers key issues Python programmers run into when naming new projects. We'll go over the following:
* Commonly used naming schemas in the Python community
* Current and past project names (including those that many newcomers to Python struggle with)
* Techniques to avoid similar confusion in the future (covering both name selection and documentation)
We'll even talk about Monty Python and its long-term impact on the Python programming language.
A Python conference north of the Golden Gate
North Bay Python is a single-track conference with a carefully curated set of talks representing the diverse Python community and their different areas of interest.
If a topic is less to your interest, or you've met some people you really want to sit down and chat with, we'll have plenty of areas away from the main theatre to catch up and chat.
Our goal is to keep prices as low as possible. That means we won't be catering lunch. Instead, you can look forward to extra-long lunch breaks you can use to explore all of the great food options around the venue.
- published: 16 Nov 2018
- views: 175
29:22
IWC 2020: Safety and Completeness of Disambiguation corresponds to Termination ... (Eelco Visser)
- Order: Reorder
- Duration: 29:22
- Uploaded Date: 05 Jul 2020
- views: 51
Talk 11
Full title: Safety and Completeness of Disambiguation corresponds to Termination and Confluence of Reordering
Talk 11
Full title: Safety and Completeness of Disambiguation corresponds to Termination and Confluence of Reordering
https://wn.com/Iwc_2020_Safety_And_Completeness_Of_Disambiguation_Corresponds_To_Termination_..._(Eelco_Visser)
Talk 11
Full title: Safety and Completeness of Disambiguation corresponds to Termination and Confluence of Reordering
- published: 05 Jul 2020
- views: 51
51:14
How to perform disambiguation of words with WordStat - Content Analysis and Text Mining Software
- Order: Reorder
- Duration: 51:14
- Uploaded Date: 11 Sep 2017
- views: 1089
Learn how to perform disambiguation with WordStat using phrases or rules.
To learn more about WordStat: https://provalisresearch.com/products/content-analysis-s...
Learn how to perform disambiguation with WordStat using phrases or rules.
To learn more about WordStat: https://provalisresearch.com/products/content-analysis-software/
#textmining #contentanalysis #NLP
https://wn.com/How_To_Perform_Disambiguation_Of_Words_With_Wordstat_Content_Analysis_And_Text_Mining_Software
Learn how to perform disambiguation with WordStat using phrases or rules.
To learn more about WordStat: https://provalisresearch.com/products/content-analysis-software/
#textmining #contentanalysis #NLP
- published: 11 Sep 2017
- views: 1089
1:02:07
Adjusting sense representations for knowledge-based word sense disambiguation
- Order: Reorder
- Duration: 1:02:07
- Uploaded Date: 31 May 2017
- views: 457
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word ca...
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word carries in a particular context – is a core research problem in computational linguistics. Though it has long been recognized that supervised (i.e., machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and text domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs) such as dictionaries and thesauri. These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. In this seminar, we treat the task of extending the efficacy and applicability of knowledge-based WSD, both generally and for the particular case of English puns. In the first part of the talk, we present two approaches for bridging the information gap and thereby improving WSD coverage and accuracy. In the first approach, we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. In the second part of the talk, we describe how these techniques, along with evaluation methodologies from traditional WSD, can be adapted for the "disambiguation" of puns, or rather for the automatic identification of their double meanings.
https://wn.com/Adjusting_Sense_Representations_For_Knowledge_Based_Word_Sense_Disambiguation
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word carries in a particular context – is a core research problem in computational linguistics. Though it has long been recognized that supervised (i.e., machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and text domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs) such as dictionaries and thesauri. These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. In this seminar, we treat the task of extending the efficacy and applicability of knowledge-based WSD, both generally and for the particular case of English puns. In the first part of the talk, we present two approaches for bridging the information gap and thereby improving WSD coverage and accuracy. In the first approach, we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. In the second part of the talk, we describe how these techniques, along with evaluation methodologies from traditional WSD, can be adapted for the "disambiguation" of puns, or rather for the automatic identification of their double meanings.
- published: 31 May 2017
- views: 457
47:23
Mod-01 Lec-30 Wordnet and Word Sense Disambiguation(contd...)
- Order: Reorder
- Duration: 47:23
- Uploaded Date: 03 Jul 2012
- views: 4670
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel...
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
https://wn.com/Mod_01_Lec_30_Wordnet_And_Word_Sense_Disambiguation(Contd...)
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
- published: 03 Jul 2012
- views: 4670
11:18
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (Research Paper Walkthrough)
- Order: Reorder
- Duration: 11:18
- Uploaded Date: 07 Apr 2021
- views: 2118
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using t...
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using the Gloss knowledge. They show state-of-art results on benchmark datasets.
⏩ Abstract: Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a supervised neural WSD system. We construct context-gloss pairs and propose three BERT-based models for WSD. We fine-tune the pre-trained BERT model on SemCor3.0 training corpus and the experimental results on several English all-words WSD benchmark datasets show that our approach outperforms the state-of-the-art systems.
Please feel free to share out the content and subscribe to my channel :)
⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1
⏩ OUTLINE:
0:00 - Abstract
01:46 - Task Definition
02:11 - Data Collection approach
02:30 - WordNet Overview
03:35 - Sentence construction method table overview
05:27 - BERT(Token-CLS)
06:41 - GlossBERT
07:52 - Context-Gloss Pair with Weak Supervision
08:55 - GlossBERT(Token-CLS)
09:20 - GlossBERT(Sent-CLS)
09:44 - GlossBERT(Sent-CLS-WS)
10:09 - Results
⏩ Paper Title: GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
⏩ Paper: https://arxiv.org/abs/1908.07245v4
⏩ Code: https://github.com/HSLCY/GlossBERT
⏩ Author: Luyao Huang, Chi Sun, Xipeng Qiu, Xuanjing Huang
⏩ Organisation: Fudan University
⏩ IMPORTANT LINKS
Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ
Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf
Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu
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Please feel free to share out the content and subscribe to my channel :)
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Tools I use for making videos :)
⏩ iPad - https://tinyurl.com/y39p6pwc
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#techviz #datascienceguy #ai #researchpaper #naturallanguageprocessing #bart
https://wn.com/Glossbert_Bert_For_Word_Sense_Disambiguation_With_Gloss_Knowledge_(Research_Paper_Walkthrough)
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using the Gloss knowledge. They show state-of-art results on benchmark datasets.
⏩ Abstract: Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a supervised neural WSD system. We construct context-gloss pairs and propose three BERT-based models for WSD. We fine-tune the pre-trained BERT model on SemCor3.0 training corpus and the experimental results on several English all-words WSD benchmark datasets show that our approach outperforms the state-of-the-art systems.
Please feel free to share out the content and subscribe to my channel :)
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⏩ OUTLINE:
0:00 - Abstract
01:46 - Task Definition
02:11 - Data Collection approach
02:30 - WordNet Overview
03:35 - Sentence construction method table overview
05:27 - BERT(Token-CLS)
06:41 - GlossBERT
07:52 - Context-Gloss Pair with Weak Supervision
08:55 - GlossBERT(Token-CLS)
09:20 - GlossBERT(Sent-CLS)
09:44 - GlossBERT(Sent-CLS-WS)
10:09 - Results
⏩ Paper Title: GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
⏩ Paper: https://arxiv.org/abs/1908.07245v4
⏩ Code: https://github.com/HSLCY/GlossBERT
⏩ Author: Luyao Huang, Chi Sun, Xipeng Qiu, Xuanjing Huang
⏩ Organisation: Fudan University
⏩ IMPORTANT LINKS
Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ
Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf
Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu
*********************************************
If you want to support me financially which totally optional and voluntary :) ❤️
You can consider buying me chai ( because i don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizCoffee
*********************************************
⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy
⏩ Blog - https://prakhartechviz.blogspot.com
⏩ LinkedIn - https://linkedin.com/in/prakhar21
⏩ Medium - https://medium.com/@prakhar.mishra
⏩ GitHub - https://github.com/prakhar21
⏩ Twitter - https://twitter.com/rattller
*********************************************
Please feel free to share out the content and subscribe to my channel :)
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Tools I use for making videos :)
⏩ iPad - https://tinyurl.com/y39p6pwc
⏩ Apple Pencil - https://tinyurl.com/y5rk8txn
⏩ GoodNotes - https://tinyurl.com/y627cfsa
#techviz #datascienceguy #ai #researchpaper #naturallanguageprocessing #bart
- published: 07 Apr 2021
- views: 2118
49:16
Mod-01 Lec-31 Wordnet; Metonymy and Word Sense Disambiguation
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science...
published: 03 Jul 2012
Mod-01 Lec-31 Wordnet; Metonymy and Word Sense Disambiguation
Mod-01 Lec-31 Wordnet; Metonymy and Word Sense Disambiguation
- Report rights infringement
- published: 03 Jul 2012
- views: 3088
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
24:46
KGC 2022: KG-Based Approach to Named Entity Disambiguation for Healthcare Applications — GraphAware
Senior Data Scientist at GraphAware, Giuseppe Futia, shows how to leverage a Named Entity ...
published: 03 Nov 2022
KGC 2022: KG-Based Approach to Named Entity Disambiguation for Healthcare Applications — GraphAware
KGC 2022: KG-Based Approach to Named Entity Disambiguation for Healthcare Applications — GraphAware
- Report rights infringement
- published: 03 Nov 2022
- views: 393
Senior Data Scientist at GraphAware, Giuseppe Futia, shows how to leverage a Named Entity Disambiguation (NED) system to disambiguate named entities in the healthcare domain and combine multiple knowledge graphs and ontologies in a single valuable source of truth.
The approach incorporates node embeddings into the NED model, employing the KG structure for the training process.
The tool can support different healthcare applications, including literature search and retrieval, clinical decision-making, relational knowledge findings, chatbots for health assistance, and recommendation tools for patients and medical practitioners.
Giuseppe Futia holds a Ph.D. in Computer Engineering from the Politecnico di Torino, where he explored Graph Representation Learning techniques to support the automatic building of Knowledge Graphs.
The 5 key takeaways:
1. The components and requirements of the Intelligent Advisory Systems (IAS).
2. How they use Hume, the Neo4j-backed no-code knowledge graph ecosystem.
3. Delving into diabetes real-life use cases and linking to the Unified Medical Language System.
4. How GraphAware utilizes ontology-based enrichment for their knowledge graph-based approach.
5. The cooperation of NED candidates selections and NED candidates ranking.
#biotechnology #lifescience #technology
22:12
[PLDI24] Static Analysis for Checking the Disambiguation Robustness of Regular Expressions
Static Analysis for Checking the Disambiguation Robustness of Regular Expressions (Video, ...
published: 23 Jul 2024
[PLDI24] Static Analysis for Checking the Disambiguation Robustness of Regular Expressions
[PLDI24] Static Analysis for Checking the Disambiguation Robustness of Regular Expressions
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- published: 23 Jul 2024
- views: 154
Static Analysis for Checking the Disambiguation Robustness of Regular Expressions (Video, PLDI 2024)
Konstantinos Mamouras, Alexis Le Glaunec, Wu Angela Li, and Agnishom Chattopadhyay
(Rice University, USA; Rice University, USA; Rice University, USA; Rice University, USA)
Abstract: Regular expressions are commonly used for finding and extracting matches from sequence data. Due to the inherent ambiguity of regular expressions, a disambiguation policy must be considered for the match extraction problem, in order to uniquely determine the desired match out of the possibly many matches. The most common disambiguation policies are the POSIX policy and the greedy (PCRE) policy. The POSIX policy chooses the longest match out of the leftmost ones. The greedy policy chooses a leftmost match and further disambiguates using a greedy interpretation of Kleene iteration to match as many times as possible. The choice of disambiguation policy can affect the output of match extraction, which can be an issue for reusing regular expressions across regex engines. In this paper, we introduce and study the notion of disambiguation robustness for regular expressions. A regular expression is robust if its extraction semantics is indifferent to whether the POSIX or greedy disambiguation policy is chosen. This gives rise to a decision problem for regular expressions, which we prove to be PSPACE-complete. We propose a static analysis algorithm for checking the (non-)robustness of regular expressions and two performance optimizations. We have implemented the proposed algorithms and we have shown experimentally that they are practical for analyzing large datasets of regular expressions derived from various application domains.
Article: https://doi.org/10.1145/3656461
ORCID: https://orcid.org/0000-0003-1209-7738, https://orcid.org/0000-0002-5444-5924, https://orcid.org/0000-0002-4523-3401, https://orcid.org/0009-0007-0462-8080
Video Tags: regex, automata, parsing, disambiguation strategy, static analysis, pldi24main-p806-p, doi:10.1145/3656461, orcid:0000-0003-1209-7738, orcid:0000-0002-5444-5924, orcid:0000-0002-4523-3401, orcid:0009-0007-0462-8080
Presentation at the PLDI 2024 conference, June 24–28, 2024, https://pldi24.sigplan.org/
Sponsored by ACM SIGPLAN,
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: 545
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.
26:51
Disambiguation, In-Jokes, and Name Collisions: What You Need to Know When Naming a Python Project
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers...
published: 16 Nov 2018
Disambiguation, In-Jokes, and Name Collisions: What You Need to Know When Naming a Python Project
Disambiguation, In-Jokes, and Name Collisions: What You Need to Know When Naming a Python Project
- Report rights infringement
- published: 16 Nov 2018
- views: 175
Thursday Bram
https://2018.northbaypython.org/schedule/presentation/15/
This talk covers key issues Python programmers run into when naming new projects. We'll go over the following:
* Commonly used naming schemas in the Python community
* Current and past project names (including those that many newcomers to Python struggle with)
* Techniques to avoid similar confusion in the future (covering both name selection and documentation)
We'll even talk about Monty Python and its long-term impact on the Python programming language.
A Python conference north of the Golden Gate
North Bay Python is a single-track conference with a carefully curated set of talks representing the diverse Python community and their different areas of interest.
If a topic is less to your interest, or you've met some people you really want to sit down and chat with, we'll have plenty of areas away from the main theatre to catch up and chat.
Our goal is to keep prices as low as possible. That means we won't be catering lunch. Instead, you can look forward to extra-long lunch breaks you can use to explore all of the great food options around the venue.
29:22
IWC 2020: Safety and Completeness of Disambiguation corresponds to Termination ... (Eelco Visser)
Talk 11
Full title: Safety and Completeness of Disambiguation corresponds to Termination...
published: 05 Jul 2020
IWC 2020: Safety and Completeness of Disambiguation corresponds to Termination ... (Eelco Visser)
IWC 2020: Safety and Completeness of Disambiguation corresponds to Termination ... (Eelco Visser)
- Report rights infringement
- published: 05 Jul 2020
- views: 51
Talk 11
Full title: Safety and Completeness of Disambiguation corresponds to Termination and Confluence of Reordering
51:14
How to perform disambiguation of words with WordStat - Content Analysis and Text Mining Software
Learn how to perform disambiguation with WordStat using phrases or rules.
To learn more ab...
published: 11 Sep 2017
How to perform disambiguation of words with WordStat - Content Analysis and Text Mining Software
How to perform disambiguation of words with WordStat - Content Analysis and Text Mining Software
- Report rights infringement
- published: 11 Sep 2017
- views: 1089
Learn how to perform disambiguation with WordStat using phrases or rules.
To learn more about WordStat: https://provalisresearch.com/products/content-analysis-software/
#textmining #contentanalysis #NLP
1:02:07
Adjusting sense representations for knowledge-based word sense disambiguation
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense ...
published: 31 May 2017
Adjusting sense representations for knowledge-based word sense disambiguation
Adjusting sense representations for knowledge-based word sense disambiguation
- Report rights infringement
- published: 31 May 2017
- views: 457
Speaker: Tristan Miller, Technische Universität Darmstadt (Germany)
Abstract: Word sense disambiguation (WSD) – the task of determining which meaning a word carries in a particular context – is a core research problem in computational linguistics. Though it has long been recognized that supervised (i.e., machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and text domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs) such as dictionaries and thesauri. These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. In this seminar, we treat the task of extending the efficacy and applicability of knowledge-based WSD, both generally and for the particular case of English puns. In the first part of the talk, we present two approaches for bridging the information gap and thereby improving WSD coverage and accuracy. In the first approach, we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. In the second part of the talk, we describe how these techniques, along with evaluation methodologies from traditional WSD, can be adapted for the "disambiguation" of puns, or rather for the automatic identification of their double meanings.
47:23
Mod-01 Lec-30 Wordnet and Word Sense Disambiguation(contd...)
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science...
published: 03 Jul 2012
Mod-01 Lec-30 Wordnet and Word Sense Disambiguation(contd...)
Mod-01 Lec-30 Wordnet and Word Sense Disambiguation(contd...)
- Report rights infringement
- published: 03 Jul 2012
- views: 4670
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
11:18
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (Research Paper Walkthrough)
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP ...
published: 07 Apr 2021
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (Research Paper Walkthrough)
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge (Research Paper Walkthrough)
- Report rights infringement
- published: 07 Apr 2021
- views: 2118
#bert #wsd #wordnet
This research uses BERT for Word Sense Disambiguation use case in NLP by modeling the entire problem as sentence classification task using the Gloss knowledge. They show state-of-art results on benchmark datasets.
⏩ Abstract: Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a supervised neural WSD system. We construct context-gloss pairs and propose three BERT-based models for WSD. We fine-tune the pre-trained BERT model on SemCor3.0 training corpus and the experimental results on several English all-words WSD benchmark datasets show that our approach outperforms the state-of-the-art systems.
Please feel free to share out the content and subscribe to my channel :)
⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1
⏩ OUTLINE:
0:00 - Abstract
01:46 - Task Definition
02:11 - Data Collection approach
02:30 - WordNet Overview
03:35 - Sentence construction method table overview
05:27 - BERT(Token-CLS)
06:41 - GlossBERT
07:52 - Context-Gloss Pair with Weak Supervision
08:55 - GlossBERT(Token-CLS)
09:20 - GlossBERT(Sent-CLS)
09:44 - GlossBERT(Sent-CLS-WS)
10:09 - Results
⏩ Paper Title: GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge
⏩ Paper: https://arxiv.org/abs/1908.07245v4
⏩ Code: https://github.com/HSLCY/GlossBERT
⏩ Author: Luyao Huang, Chi Sun, Xipeng Qiu, Xuanjing Huang
⏩ Organisation: Fudan University
⏩ IMPORTANT LINKS
Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ
Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf
Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu
*********************************************
If you want to support me financially which totally optional and voluntary :) ❤️
You can consider buying me chai ( because i don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizCoffee
*********************************************
⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy
⏩ Blog - https://prakhartechviz.blogspot.com
⏩ LinkedIn - https://linkedin.com/in/prakhar21
⏩ Medium - https://medium.com/@prakhar.mishra
⏩ GitHub - https://github.com/prakhar21
⏩ Twitter - https://twitter.com/rattller
*********************************************
Please feel free to share out the content and subscribe to my channel :)
⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1
Tools I use for making videos :)
⏩ iPad - https://tinyurl.com/y39p6pwc
⏩ Apple Pencil - https://tinyurl.com/y5rk8txn
⏩ GoodNotes - https://tinyurl.com/y627cfsa
#techviz #datascienceguy #ai #researchpaper #naturallanguageprocessing #bart
Non-state (disambiguation)
Non-state describes a player on scene where states are major players.
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Non-state_(disambiguation)
Mod-01 Lec-31 Wordnet; Metonymy and Word Sense Dis...
KGC 2022: KG-Based Approach to Named Entity Disamb...
[PLDI24] Static Analysis for Checking the Disambig...
Disambiguation – Linking Data Science and Engineer...
Disambiguation, In-Jokes, and Name Collisions: Wha...
IWC 2020: Safety and Completeness of Disambiguatio...
How to perform disambiguation of words with WordSt...
Adjusting sense representations for knowledge-base...
Mod-01 Lec-30 Wordnet and Word Sense Disambiguatio...