-
Inform (disambiguation)
Inform is a programming language for interactive fiction.
Inform may also refer to:
INFORM, Inc., an environmental organization
INFORM (Information Network Focus on Religious Movements), UK
INFORM, predecessor of CorVision
What an informant or informer does
Source: https://en.wikipedia.org/wiki/Inform_(disambiguation)
Created with WikipediaReaderReborn (c) WikipediaReader
published: 20 Aug 2021
-
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
-
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
-
Word Sense Disambiguation 🔥
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right now,
more details below:
https://perfectcomputerengineer.classx.co.in/new-courses/13-natural-language-processing-notes
* Natural Language Processing Playlist:
https://youtube.com/playlist?list=PLPIwNooIb9vimsumdWeKF3BRzs9tJ-_gy
* Human-Machine Interaction entire Playlist:
https://www.youtube.com/playlist?list=PLPIwNooIb9vhFRT_3JDQ0CGbW5HeFg3yK
* Distributed Computing:
https://youtube.com/playlist?list=PLPIwNooIb9vhYroMrNpoBYiBUFzTwEZot
*Gears used for this YouTube Channel:
https://linktr.ee/perfectcomputerengineer
*Let's connect:
Instagram: https://www.instagram.com/planetojas/
published: 05 Dec 2021
-
Entity Disambiguation and Structured Data Extraction
Access Innovations' Bob Kasenchak explains how to disambiguate duplicate named entities in data extraction and conversion in this clip from his presentation at Data Summit 2019.
published: 06 Jan 2020
-
A Visual Analytics Approach to Author Name Disambiguation
Title: A Visual Analytics Approach to Author Name Disambiguation
published: 11 Oct 2016
-
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
-
ElixirConf 2021 - Vanessa Lee - And Yet Akin: Name Disambiguation in Elixir
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.
published: 23 Oct 2021
-
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
-
Word Sense Disambiguation Animated PPT Slides
Download our Word Sense Disambiguation PPT template to discuss the process of finding the meaning of an ambiguous word in the given context. -https://www.sketchbubble.com/en/presentation-word-sense-disambiguation.html
published: 29 Dec 2022
0:24
Inform (disambiguation)
Inform is a programming language for interactive fiction.
Inform may also refer to:
INFORM, Inc., an environmental organization
INFORM (Information Network Foc...
Inform is a programming language for interactive fiction.
Inform may also refer to:
INFORM, Inc., an environmental organization
INFORM (Information Network Focus on Religious Movements), UK
INFORM, predecessor of CorVision
What an informant or informer does
Source: https://en.wikipedia.org/wiki/Inform_(disambiguation)
Created with WikipediaReaderReborn (c) WikipediaReader
https://wn.com/Inform_(Disambiguation)
Inform is a programming language for interactive fiction.
Inform may also refer to:
INFORM, Inc., an environmental organization
INFORM (Information Network Focus on Religious Movements), UK
INFORM, predecessor of CorVision
What an informant or informer does
Source: https://en.wikipedia.org/wiki/Inform_(disambiguation)
Created with WikipediaReaderReborn (c) WikipediaReader
- published: 20 Aug 2021
- views: 0
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 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
*********************************************
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
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 :)
⏩ 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
- published: 07 Apr 2021
- views: 2095
4:54
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 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: 1065
8:29
Word Sense Disambiguation 🔥
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right ...
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right now,
more details below:
https://perfectcomputerengineer.classx.co.in/new-courses/13-natural-language-processing-notes
* Natural Language Processing Playlist:
https://youtube.com/playlist?list=PLPIwNooIb9vimsumdWeKF3BRzs9tJ-_gy
* Human-Machine Interaction entire Playlist:
https://www.youtube.com/playlist?list=PLPIwNooIb9vhFRT_3JDQ0CGbW5HeFg3yK
* Distributed Computing:
https://youtube.com/playlist?list=PLPIwNooIb9vhYroMrNpoBYiBUFzTwEZot
*Gears used for this YouTube Channel:
https://linktr.ee/perfectcomputerengineer
*Let's connect:
Instagram: https://www.instagram.com/planetojas/
https://wn.com/Word_Sense_Disambiguation_🔥
This video tutorial is about Word Sense Disambiguation in Natural Language Processing ( nlp ) in the language Hindi using lesk algorithm.
Purchase notes right now,
more details below:
https://perfectcomputerengineer.classx.co.in/new-courses/13-natural-language-processing-notes
* Natural Language Processing Playlist:
https://youtube.com/playlist?list=PLPIwNooIb9vimsumdWeKF3BRzs9tJ-_gy
* Human-Machine Interaction entire Playlist:
https://www.youtube.com/playlist?list=PLPIwNooIb9vhFRT_3JDQ0CGbW5HeFg3yK
* Distributed Computing:
https://youtube.com/playlist?list=PLPIwNooIb9vhYroMrNpoBYiBUFzTwEZot
*Gears used for this YouTube Channel:
https://linktr.ee/perfectcomputerengineer
*Let's connect:
Instagram: https://www.instagram.com/planetojas/
- published: 05 Dec 2021
- views: 62029
5:47
Entity Disambiguation and Structured Data Extraction
Access Innovations' Bob Kasenchak explains how to disambiguate duplicate named entities in data extraction and conversion in this clip from his presentation at ...
Access Innovations' Bob Kasenchak explains how to disambiguate duplicate named entities in data extraction and conversion in this clip from his presentation at Data Summit 2019.
https://wn.com/Entity_Disambiguation_And_Structured_Data_Extraction
Access Innovations' Bob Kasenchak explains how to disambiguate duplicate named entities in data extraction and conversion in this clip from his presentation at Data Summit 2019.
- published: 06 Jan 2020
- views: 298
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 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: 389
28:54
ElixirConf 2021 - Vanessa Lee - And Yet Akin: Name Disambiguation in Elixir
Synonymity and homonymity make name disambiguation difficult. To ease this difficulty, I combined two unmaintained Elixir string comparison libraries and added ...
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.
https://wn.com/Elixirconf_2021_Vanessa_Lee_And_Yet_Akin_Name_Disambiguation_In_Elixir
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.
- published: 23 Oct 2021
- views: 509
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-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: 539
0:29
Word Sense Disambiguation Animated PPT Slides
Download our Word Sense Disambiguation PPT template to discuss the process of finding the meaning of an ambiguous word in the given context. -https://www.sketch...
Download our Word Sense Disambiguation PPT template to discuss the process of finding the meaning of an ambiguous word in the given context. -https://www.sketchbubble.com/en/presentation-word-sense-disambiguation.html
https://wn.com/Word_Sense_Disambiguation_Animated_Ppt_Slides
Download our Word Sense Disambiguation PPT template to discuss the process of finding the meaning of an ambiguous word in the given context. -https://www.sketchbubble.com/en/presentation-word-sense-disambiguation.html
- published: 29 Dec 2022
- views: 484