-
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
-
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
-
Carrie Underwood - How great thou art (feat. Vince Gill) 2011 ACM Girls Night Out
This performance comes from the “2011 ACM Girls Night Out: Superstar Women of Country” concert, which aired on CBS in 2011. It features Vince Gill joining Carrie singing the chorus, and playing an extended guitar solo where verses 2 and 3 of the traditional hymn would typically go.
I DO NOT HAVE THE COPYRIGHT OF THIS VIDEO AND MUSIC. PLEASE ADVISE TO REMOVE IMMEDIATELY IF ANY INFRINGEMENT CAUSED. THANK YOU !
published: 13 Feb 2018
-
093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning...
published: 30 Nov 2022
-
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
-
Illuminator(Illuminate) Day 2 Drums and Keys Disambiguation
this is the second update from the underoath website
http://www.underoathh.com/
ive figured it out that the song name is Illuminator
once again i do not own this song or claim any rights to it,
underoath has made this music and their record label and them own it
(solid state) tooth and nail recording
published: 29 Sep 2010
-
Lecture 41 — Word Sense Disambiguation - Natural Language Processing | Michigan
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclan...
published: 27 Mar 2016
-
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
-
How We Decoded The Hieroglyphs Of Ancient Egypt
'How We Decoded The Hieroglyphs Of Ancient Egypt'
In this clip from the History Hit documentary 'The Story of Egyptology', Dr Chris Naunton explores how 18th century scholars worked frantically to decode the secrets of Ancient Egyptian hieroglyphs after the discovery of the Rosetta Stone.
Watch the full episode now on History Hit TV: https://access.historyhit.com/what-s-new/videos/story-of-egyptology
Egyptologist Dr Chris Naunton explores the story of how Ancient Egypt was rediscovered, and how its incredible sites and treasures were gradually decoded. Starting with the earliest travellers who ventured inside the pyramids, Chris traces how this curiosity exploded into Egyptomania in the 18th and 19th centuries. Beginning with the French invasion under Napoleon, we discover how Egypt wa...
published: 27 May 2022
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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
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
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: 1130
4:22
Carrie Underwood - How great thou art (feat. Vince Gill) 2011 ACM Girls Night Out
This performance comes from the “2011 ACM Girls Night Out: Superstar Women of Country” concert, which aired on CBS in 2011. It features Vince Gill joining Carri...
This performance comes from the “2011 ACM Girls Night Out: Superstar Women of Country” concert, which aired on CBS in 2011. It features Vince Gill joining Carrie singing the chorus, and playing an extended guitar solo where verses 2 and 3 of the traditional hymn would typically go.
I DO NOT HAVE THE COPYRIGHT OF THIS VIDEO AND MUSIC. PLEASE ADVISE TO REMOVE IMMEDIATELY IF ANY INFRINGEMENT CAUSED. THANK YOU !
https://wn.com/Carrie_Underwood_How_Great_Thou_Art_(Feat._Vince_Gill)_2011_Acm_Girls_Night_Out
This performance comes from the “2011 ACM Girls Night Out: Superstar Women of Country” concert, which aired on CBS in 2011. It features Vince Gill joining Carrie singing the chorus, and playing an extended guitar solo where verses 2 and 3 of the traditional hymn would typically go.
I DO NOT HAVE THE COPYRIGHT OF THIS VIDEO AND MUSIC. PLEASE ADVISE TO REMOVE IMMEDIATELY IF ANY INFRINGEMENT CAUSED. THANK YOU !
- published: 13 Feb 2018
- views: 8668880
35:11
093 Keyword Disambiguation Using Transformers and Clustering to Build Cleaner Knowledge - NODES2022
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities ...
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning, #Visualization, #General, #Advanced
Region: EMEA
Slides: https://dist.neo4j.com/nodes-20202-slides/093%20Keyword%20Disambiguation%20Using%20Transformers%20and%20Clustering%20to%20Build%20Cleaner%20Knowledge%20Graphs%20-%20NODES2022%20EMEA%20Advanced%206%20-%20Federica%20Ventruto%2C%20Alessia%20Melania%20Lonoce.pdf
Visit https://neo4j.com/nodes-2022 learn more at https://neo4j.com/developer/get-started and engage at https://community.neo4j.com
https://wn.com/093_Keyword_Disambiguation_Using_Transformers_And_Clustering_To_Build_Cleaner_Knowledge_Nodes2022
Natural language processing is an indispensable toolkit to build knowledge graphs from unstructured data. However, it comes with a price. Keywords and entities in unstructured texts are ambiguous - the same concept can be expressed by many different linguistic variations. The resulting knowledge graph would thus be polluted with many nodes representing the same entity without any order. In this session, we show how the semantic similarity based on transformer embeddings and agglomerative clustering can help in the domain of academic disciplines and research fields and how Neo4j improves the browsing experience of this knowledge graph.
Speakers: Federica Ventruto, Alessia Melania Lonoce
Format: Full Session 30-45 min
Level: Advanced
Topics: #KnowledgeGraph, #MachineLearning, #Visualization, #General, #Advanced
Region: EMEA
Slides: https://dist.neo4j.com/nodes-20202-slides/093%20Keyword%20Disambiguation%20Using%20Transformers%20and%20Clustering%20to%20Build%20Cleaner%20Knowledge%20Graphs%20-%20NODES2022%20EMEA%20Advanced%206%20-%20Federica%20Ventruto%2C%20Alessia%20Melania%20Lonoce.pdf
Visit https://neo4j.com/nodes-2022 learn more at https://neo4j.com/developer/get-started and engage at https://community.neo4j.com
- published: 30 Nov 2022
- views: 515
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
3:16
Illuminator(Illuminate) Day 2 Drums and Keys Disambiguation
this is the second update from the underoath website
http://www.underoathh.com/
ive figured it out that the song name is Illuminator
once again i do not ow...
this is the second update from the underoath website
http://www.underoathh.com/
ive figured it out that the song name is Illuminator
once again i do not own this song or claim any rights to it,
underoath has made this music and their record label and them own it
(solid state) tooth and nail recording
https://wn.com/Illuminator(Illuminate)_Day_2_Drums_And_Keys_Disambiguation
this is the second update from the underoath website
http://www.underoathh.com/
ive figured it out that the song name is Illuminator
once again i do not own this song or claim any rights to it,
underoath has made this music and their record label and them own it
(solid state) tooth and nail recording
- published: 29 Sep 2010
- views: 2746
20:07
Lecture 41 — Word Sense Disambiguation - Natural Language Processing | Michigan
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (ht...
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclanthology.org/W17-2407.pdf
Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation"
Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106
Paper Title: "Neural Diverse Abstractive Sentence Compression Generation"
Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14
https://wn.com/Lecture_41_—_Word_Sense_Disambiguation_Natural_Language_Processing_|_Michigan
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclanthology.org/W17-2407.pdf
Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation"
Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106
Paper Title: "Neural Diverse Abstractive Sentence Compression Generation"
Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14
- published: 27 Mar 2016
- views: 14931
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
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- published: 07 Apr 2021
- views: 2095
10:17
How We Decoded The Hieroglyphs Of Ancient Egypt
'How We Decoded The Hieroglyphs Of Ancient Egypt'
In this clip from the History Hit documentary 'The Story of Egyptology', Dr Chris Naunton explores how 18th c...
'How We Decoded The Hieroglyphs Of Ancient Egypt'
In this clip from the History Hit documentary 'The Story of Egyptology', Dr Chris Naunton explores how 18th century scholars worked frantically to decode the secrets of Ancient Egyptian hieroglyphs after the discovery of the Rosetta Stone.
Watch the full episode now on History Hit TV: https://access.historyhit.com/what-s-new/videos/story-of-egyptology
Egyptologist Dr Chris Naunton explores the story of how Ancient Egypt was rediscovered, and how its incredible sites and treasures were gradually decoded. Starting with the earliest travellers who ventured inside the pyramids, Chris traces how this curiosity exploded into Egyptomania in the 18th and 19th centuries. Beginning with the French invasion under Napoleon, we discover how Egypt was explored, plundered and eventually deciphered as increasingly scientific approaches were taken. Highlights include the audacious treasure hunting by Belzoni, the painstaking decoding of hieroglyphs and Flinders Petrie's introduction of modern methodology - all leading to Howard Carter's opening of the tomb of Tutankhamun.
Sign up to History Hit TV now and get 7 days free: http://access.historyhit.com/checkout
https://wn.com/How_We_Decoded_The_Hieroglyphs_Of_Ancient_Egypt
'How We Decoded The Hieroglyphs Of Ancient Egypt'
In this clip from the History Hit documentary 'The Story of Egyptology', Dr Chris Naunton explores how 18th century scholars worked frantically to decode the secrets of Ancient Egyptian hieroglyphs after the discovery of the Rosetta Stone.
Watch the full episode now on History Hit TV: https://access.historyhit.com/what-s-new/videos/story-of-egyptology
Egyptologist Dr Chris Naunton explores the story of how Ancient Egypt was rediscovered, and how its incredible sites and treasures were gradually decoded. Starting with the earliest travellers who ventured inside the pyramids, Chris traces how this curiosity exploded into Egyptomania in the 18th and 19th centuries. Beginning with the French invasion under Napoleon, we discover how Egypt was explored, plundered and eventually deciphered as increasingly scientific approaches were taken. Highlights include the audacious treasure hunting by Belzoni, the painstaking decoding of hieroglyphs and Flinders Petrie's introduction of modern methodology - all leading to Howard Carter's opening of the tomb of Tutankhamun.
Sign up to History Hit TV now and get 7 days free: http://access.historyhit.com/checkout
- published: 27 May 2022
- views: 543149
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 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