-
Arya's Death List and Disambiguation #shorts
The order of Arya's death list in A Song of Ice and Fire actually relies a bit on disambiguation...
▬▬▬▬ Follow Me on Social Media! ▬▬▬▬
https://www.facebook.com/prestonjacobssweetrobin/
https://twitter.com/sweetrobin9000
https://windsofwinter.com
▬▬▬▬ Check Out These Videos! ▬▬▬▬
The Purple Wedding: https://www.youtube.com/watch?v=tkIczwc7Hz8
A Frey in the Snow: https://www.youtube.com/watch?v=_CaDHo9BsJI&
The Deeper Dorne: https://www.youtube.com/watch?v=55N8Q6OINHg&t=1s
#asongoficeandfire #gameofthrones #houseofthedragon
published: 01 Feb 2023
-
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
-
What goes into the disambiguation process? | Xapien
Imagine you’ve got two news articles, and you need to figure out which ones are talking about your subject. As a human, you’ll take what you know about the person you’re researching and then cross-reference that knowledge with the information inside the two articles. As you read the articles, you’ll pay attention to aspects like people, locations, organisations, and roles.
Our brain relies on "signals," which are pieces of information we identify and extract. These signals, comprising key details, patterns, and insights, help us decide what information is important and what can be ignored.
Searching using keywords, assessing source credibility, skimming for main points, cross-referencing information, and looking at photos all involve extracting signals and using our “sensors”. As human...
published: 17 May 2024
-
【Fukase】 - Disambiguation 【Vocaloidカバー】2019 + (Engl. Sub)
Phew.. late night Upload. I know its not perfect but i really tried to tune it!
(i really hope this turned out decent) Leave me a comment if you liked it!
LUMi's Version: https://www.youtube.com/watch?v=PT8fj7M1zb0
At first i've wanted Ken to sing this so i made this in V5.. after a while i choose Lumi then Len and now its Fukase lol! (but V5 = no XSY -,-)
I still think Lumi and Len sound very nice here (especially Len o_o) and maybe i will upload their versions later! Tell me if you want to hear them.
Please note that this cover is not to be used without a granted request. This include reuploads to other platforms etc. Please respect that.
I think my next Project will be with VY2 again... but who knows o_o;
======================================================
Original by Polic...
published: 18 Feb 2019
-
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
-
Zmavli Caimle - Generality of lexical disambiguation of noun-noun homographs in GPT2-small
In this presentation, Zmavli Caimle talks about how he looked into GPT2-small to figure out the parts of it that perform noun-noun disambiguation (e.g., to know whether “bat” refers to an animal or object). He also answers research-related questions from the audience after his presentation.
—
Zmavli is a high school dropout and a fluent Lojban speaker. With the help of this Lojban fluency, he was able to do contract work on an AI safety project of an Open Philanthropy grantee. He has also been familiar with effective altruism and rationality for years.
Zmavli also won 3rd place in an Apart Research AI safety hackathon along with three other WhiteBox fellows. Their project showed how some models would presume questions as “safe” even though they’re high risk for dangerous use.
—
Learn ...
published: 14 Sep 2024
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Entity Disambiguation with Extreme Multi label Ranking
Jyun-Yu Jiang, Amazon Search, Palo Alto, USA
published: 23 Jul 2024
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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
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Disambiguation: The Black Technology
Zhihoa Yuan's presentation from C++Now 2014.
Slides are available here: https://github.com/boostcon/cppnow_presentations_2014/blob/master/files/disambiguation.pdf?raw=true
---
*--*
---
published: 26 Sep 2014
-
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
0:59
Arya's Death List and Disambiguation #shorts
The order of Arya's death list in A Song of Ice and Fire actually relies a bit on disambiguation...
▬▬▬▬ Follow Me on Social Media! ▬▬▬▬
https://www.facebook.c...
The order of Arya's death list in A Song of Ice and Fire actually relies a bit on disambiguation...
▬▬▬▬ Follow Me on Social Media! ▬▬▬▬
https://www.facebook.com/prestonjacobssweetrobin/
https://twitter.com/sweetrobin9000
https://windsofwinter.com
▬▬▬▬ Check Out These Videos! ▬▬▬▬
The Purple Wedding: https://www.youtube.com/watch?v=tkIczwc7Hz8
A Frey in the Snow: https://www.youtube.com/watch?v=_CaDHo9BsJI&
The Deeper Dorne: https://www.youtube.com/watch?v=55N8Q6OINHg&t=1s
#asongoficeandfire #gameofthrones #houseofthedragon
https://wn.com/Arya's_Death_List_And_Disambiguation_Shorts
The order of Arya's death list in A Song of Ice and Fire actually relies a bit on disambiguation...
▬▬▬▬ Follow Me on Social Media! ▬▬▬▬
https://www.facebook.com/prestonjacobssweetrobin/
https://twitter.com/sweetrobin9000
https://windsofwinter.com
▬▬▬▬ Check Out These Videos! ▬▬▬▬
The Purple Wedding: https://www.youtube.com/watch?v=tkIczwc7Hz8
A Frey in the Snow: https://www.youtube.com/watch?v=_CaDHo9BsJI&
The Deeper Dorne: https://www.youtube.com/watch?v=55N8Q6OINHg&t=1s
#asongoficeandfire #gameofthrones #houseofthedragon
- published: 01 Feb 2023
- views: 20485
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 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
2:02
What goes into the disambiguation process? | Xapien
Imagine you’ve got two news articles, and you need to figure out which ones are talking about your subject. As a human, you’ll take what you know about the pers...
Imagine you’ve got two news articles, and you need to figure out which ones are talking about your subject. As a human, you’ll take what you know about the person you’re researching and then cross-reference that knowledge with the information inside the two articles. As you read the articles, you’ll pay attention to aspects like people, locations, organisations, and roles.
Our brain relies on "signals," which are pieces of information we identify and extract. These signals, comprising key details, patterns, and insights, help us decide what information is important and what can be ignored.
Searching using keywords, assessing source credibility, skimming for main points, cross-referencing information, and looking at photos all involve extracting signals and using our “sensors”. As humans, we typically extract around 30 signals from text, whether consciously or unconsciously. We've taught our AI how to recognise these signals and draw the dots.
https://wn.com/What_Goes_Into_The_Disambiguation_Process_|_Xapien
Imagine you’ve got two news articles, and you need to figure out which ones are talking about your subject. As a human, you’ll take what you know about the person you’re researching and then cross-reference that knowledge with the information inside the two articles. As you read the articles, you’ll pay attention to aspects like people, locations, organisations, and roles.
Our brain relies on "signals," which are pieces of information we identify and extract. These signals, comprising key details, patterns, and insights, help us decide what information is important and what can be ignored.
Searching using keywords, assessing source credibility, skimming for main points, cross-referencing information, and looking at photos all involve extracting signals and using our “sensors”. As humans, we typically extract around 30 signals from text, whether consciously or unconsciously. We've taught our AI how to recognise these signals and draw the dots.
- published: 17 May 2024
- views: 49
3:28
【Fukase】 - Disambiguation 【Vocaloidカバー】2019 + (Engl. Sub)
Phew.. late night Upload. I know its not perfect but i really tried to tune it!
(i really hope this turned out decent) Leave me a comment if you liked it!
LUM...
Phew.. late night Upload. I know its not perfect but i really tried to tune it!
(i really hope this turned out decent) Leave me a comment if you liked it!
LUMi's Version: https://www.youtube.com/watch?v=PT8fj7M1zb0
At first i've wanted Ken to sing this so i made this in V5.. after a while i choose Lumi then Len and now its Fukase lol! (but V5 = no XSY -,-)
I still think Lumi and Len sound very nice here (especially Len o_o) and maybe i will upload their versions later! Tell me if you want to hear them.
Please note that this cover is not to be used without a granted request. This include reuploads to other platforms etc. Please respect that.
I think my next Project will be with VY2 again... but who knows o_o;
======================================================
Original by Police Piccadilly ft. Yamine Renri
Subtitles by Wordhuntering
Translation by Hazuki no Yume
(Subtitles from Hazuki no Yume's Video)
Base UST by azumibird
Harmonie Improvments by kimchi-tan
VPR by Lamunan
Tuning & Mixing by Lamunan
Video Edit by Lamunan
Singer Fukase
======================================================
#Vocaloid #Fukase #Vocaloidカバー
https://wn.com/【Fukase】_Disambiguation_【Vocaloidカバー】2019_(Engl._Sub)
Phew.. late night Upload. I know its not perfect but i really tried to tune it!
(i really hope this turned out decent) Leave me a comment if you liked it!
LUMi's Version: https://www.youtube.com/watch?v=PT8fj7M1zb0
At first i've wanted Ken to sing this so i made this in V5.. after a while i choose Lumi then Len and now its Fukase lol! (but V5 = no XSY -,-)
I still think Lumi and Len sound very nice here (especially Len o_o) and maybe i will upload their versions later! Tell me if you want to hear them.
Please note that this cover is not to be used without a granted request. This include reuploads to other platforms etc. Please respect that.
I think my next Project will be with VY2 again... but who knows o_o;
======================================================
Original by Police Piccadilly ft. Yamine Renri
Subtitles by Wordhuntering
Translation by Hazuki no Yume
(Subtitles from Hazuki no Yume's Video)
Base UST by azumibird
Harmonie Improvments by kimchi-tan
VPR by Lamunan
Tuning & Mixing by Lamunan
Video Edit by Lamunan
Singer Fukase
======================================================
#Vocaloid #Fukase #Vocaloidカバー
- published: 18 Feb 2019
- views: 2054
1:02:07
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 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: 463
26:37
Zmavli Caimle - Generality of lexical disambiguation of noun-noun homographs in GPT2-small
In this presentation, Zmavli Caimle talks about how he looked into GPT2-small to figure out the parts of it that perform noun-noun disambiguation (e.g., to know...
In this presentation, Zmavli Caimle talks about how he looked into GPT2-small to figure out the parts of it that perform noun-noun disambiguation (e.g., to know whether “bat” refers to an animal or object). He also answers research-related questions from the audience after his presentation.
—
Zmavli is a high school dropout and a fluent Lojban speaker. With the help of this Lojban fluency, he was able to do contract work on an AI safety project of an Open Philanthropy grantee. He has also been familiar with effective altruism and rationality for years.
Zmavli also won 3rd place in an Apart Research AI safety hackathon along with three other WhiteBox fellows. Their project showed how some models would presume questions as “safe” even though they’re high risk for dangerous use.
—
Learn more about the WhiteBox Research AI Interpretability fellowship here: https://bit.ly/WBFellowship2024C1Primer
And if you’re deeply curious to explore the inner workings of AI models, you can express interest in joining Cohort 2 of our research fellowship here: https://bit.ly/WBFellowshipInterestForm
https://wn.com/Zmavli_Caimle_Generality_Of_Lexical_Disambiguation_Of_Noun_Noun_Homographs_In_Gpt2_Small
In this presentation, Zmavli Caimle talks about how he looked into GPT2-small to figure out the parts of it that perform noun-noun disambiguation (e.g., to know whether “bat” refers to an animal or object). He also answers research-related questions from the audience after his presentation.
—
Zmavli is a high school dropout and a fluent Lojban speaker. With the help of this Lojban fluency, he was able to do contract work on an AI safety project of an Open Philanthropy grantee. He has also been familiar with effective altruism and rationality for years.
Zmavli also won 3rd place in an Apart Research AI safety hackathon along with three other WhiteBox fellows. Their project showed how some models would presume questions as “safe” even though they’re high risk for dangerous use.
—
Learn more about the WhiteBox Research AI Interpretability fellowship here: https://bit.ly/WBFellowship2024C1Primer
And if you’re deeply curious to explore the inner workings of AI models, you can express interest in joining Cohort 2 of our research fellowship here: https://bit.ly/WBFellowshipInterestForm
- published: 14 Sep 2024
- views: 40
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: 401
1:26:45
Disambiguation: The Black Technology
Zhihoa Yuan's presentation from C++Now 2014.
Slides are available here: https://github.com/boostcon/cppnow_presentations_2014/blob/master/files/disambiguation.p...
Zhihoa Yuan's presentation from C++Now 2014.
Slides are available here: https://github.com/boostcon/cppnow_presentations_2014/blob/master/files/disambiguation.pdf?raw=true
---
*--*
---
https://wn.com/Disambiguation_The_Black_Technology
Zhihoa Yuan's presentation from C++Now 2014.
Slides are available here: https://github.com/boostcon/cppnow_presentations_2014/blob/master/files/disambiguation.pdf?raw=true
---
*--*
---
- published: 26 Sep 2014
- views: 799
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: 564