A symbol identifying a genetic lineage as a paragroup of a specified haplogroup
Star (game theory), the value given to the game where both players have only the option of moving to the zero game
In linguistics, a symbol that prefixes a word or phrase that, in historical linguistics, is a reconstructed form for which no actual examples have been found; and in linguistics of a modern language (see: synchronic linguistics), is judged ungrammatical
The symbol is used to refer a reader to a footnote
The symbol is used to refer a reader to an endnote
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
A Visual Analytics Approach to Author Name Disambiguation
Title: A Visual Analytics Approach to Author Name Disambiguation
published: 11 Oct 2016
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee | Code BEAM America 2021
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee - Senior Software Engineer at Interfolio
ABSTRACT
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.
OBJECTIVES:
To introduce the ...
published: 16 Sep 2022
A Visual Approach for Name Disambiguation in Coauthorship Networks
published: 17 Oct 2018
Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop
Authors:
Yutao Zhang (Tsinghua University); Fanjin Zhang (Tsinghua University); Peiran Yao (Tsinghua University); Jie Tang (Tsinghua University)
More on http://www.kdd.org/kdd2018/
published: 12 Jun 2018
Author Name Disambiguation Top # 6 Facts
Author Name Disambiguation Top # 6 Facts
published: 28 Oct 2015
Technical Track: gambit – An Open Source Name Disambiguation Tool for Version Control Systems
Name disambiguation is a complex but highly relevant challenge whenever analysing real-world user data, such as data from version control systems. We propose gambit, a rule-based disambiguation tool that only relies on name and email information. We evaluate its performance against two commonly used algorithms with similar characteristics, on manually disambiguated ground-truth data from the Gnome GTK project. Our results show that gambit significantly outperforms both algorithms in terms of precision as well as F1 score.
Uploaded with Clowdr https://clowdr.org/
published: 01 Jun 2021
DisamBERT: Author name disambiguation based on BERT [SciNLP poster presentation]
Scientific endeavor revolves around scientists. Yet data about scientists are notoriously inaccurate due to the challenging and ubiquitous problem of author name disambiguation. Here, we propose a new disambiguation framework based on BERT, which can automatically select the most useful features for disambiguation and achieved a decent performance. Visit our poster at SciNLP!
published: 30 Sep 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
Name disambiguation in Aminer
Name disambiguation in Aminer
Zhang, Jing; Tang, Jie
Sci China Inf Sci, 2021, 64(4): 144101
Name disambiguation, aiming at disambiguating who is who, is one of the fundamental problems of the online academic network platforms such as Google scholar, microsoft academic and AMiner. This study takes AMiner, a free online academic search and mining system, as the example to explain how we deal with the name ambiguity problem under three different scenarios. AMiner has already extracted 13 million researchers' profiles from the Web and integrated with 20 million papers from heterogeneous publication databases, with a growth rate of over 500000 per month. From the beginning when the system is built to the running and updating phases, we need to pay continuous attention on the problem of name ...
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.
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.
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanes...
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee - Senior Software Engineer at Interfolio
ABSTRACT
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.
OBJECTIVES:
To introduce the problem of name disambiguation and string comparison by looking at two existing string comparison libraries before addressing the process of combining them into a single repository. I hope attendees will leave understanding the problem as well as the strengths, limitations, and possibilities of the new library and how it can be used to address the challenges of name disambiguation.
AUDIENCE:
Beginner to intermediate programmers.
• Timecodes
00:00 - 03:54 - Intro
03:55 - 05:14 - String Comparison Algorithms
05:15 - 09:42 - Akin
09:43 - 13:20 - Axon & Training Data: DBLP
13:21 - 18:09 - NX and Axon
18:10 - 19:36 - What's next?
19:36 - 36:43 - QnA
• Follow us on social:
Twitter: https://twitter.com/CodeBEAMio
LinkedIn: https://www.linkedin.com/company/27159258
• Looking for a unique learning experience?
Attend the next Code Sync conference near you!
See what's coming up at: https://codesync.global
• SUBSCRIBE TO OUR CHANNEL
https://www.youtube.com/channel/UC47eUBNO8KBH_V8AfowOWOw
See what's coming up at: https://codesync.global
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee - Senior Software Engineer at Interfolio
ABSTRACT
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.
OBJECTIVES:
To introduce the problem of name disambiguation and string comparison by looking at two existing string comparison libraries before addressing the process of combining them into a single repository. I hope attendees will leave understanding the problem as well as the strengths, limitations, and possibilities of the new library and how it can be used to address the challenges of name disambiguation.
AUDIENCE:
Beginner to intermediate programmers.
• Timecodes
00:00 - 03:54 - Intro
03:55 - 05:14 - String Comparison Algorithms
05:15 - 09:42 - Akin
09:43 - 13:20 - Axon & Training Data: DBLP
13:21 - 18:09 - NX and Axon
18:10 - 19:36 - What's next?
19:36 - 36:43 - QnA
• Follow us on social:
Twitter: https://twitter.com/CodeBEAMio
LinkedIn: https://www.linkedin.com/company/27159258
• Looking for a unique learning experience?
Attend the next Code Sync conference near you!
See what's coming up at: https://codesync.global
• SUBSCRIBE TO OUR CHANNEL
https://www.youtube.com/channel/UC47eUBNO8KBH_V8AfowOWOw
See what's coming up at: https://codesync.global
Authors:
Yutao Zhang (Tsinghua University); Fanjin Zhang (Tsinghua University); Peiran Yao (Tsinghua University); Jie Tang (Tsinghua University)
More on http:...
Authors:
Yutao Zhang (Tsinghua University); Fanjin Zhang (Tsinghua University); Peiran Yao (Tsinghua University); Jie Tang (Tsinghua University)
More on http://www.kdd.org/kdd2018/
Authors:
Yutao Zhang (Tsinghua University); Fanjin Zhang (Tsinghua University); Peiran Yao (Tsinghua University); Jie Tang (Tsinghua University)
More on http://www.kdd.org/kdd2018/
Name disambiguation is a complex but highly relevant challenge whenever analysing real-world user data, such as data from version control systems. We propose ga...
Name disambiguation is a complex but highly relevant challenge whenever analysing real-world user data, such as data from version control systems. We propose gambit, a rule-based disambiguation tool that only relies on name and email information. We evaluate its performance against two commonly used algorithms with similar characteristics, on manually disambiguated ground-truth data from the Gnome GTK project. Our results show that gambit significantly outperforms both algorithms in terms of precision as well as F1 score.
Uploaded with Clowdr https://clowdr.org/
Name disambiguation is a complex but highly relevant challenge whenever analysing real-world user data, such as data from version control systems. We propose gambit, a rule-based disambiguation tool that only relies on name and email information. We evaluate its performance against two commonly used algorithms with similar characteristics, on manually disambiguated ground-truth data from the Gnome GTK project. Our results show that gambit significantly outperforms both algorithms in terms of precision as well as F1 score.
Uploaded with Clowdr https://clowdr.org/
Scientific endeavor revolves around scientists. Yet data about scientists are notoriously inaccurate due to the challenging and ubiquitous problem of author nam...
Scientific endeavor revolves around scientists. Yet data about scientists are notoriously inaccurate due to the challenging and ubiquitous problem of author name disambiguation. Here, we propose a new disambiguation framework based on BERT, which can automatically select the most useful features for disambiguation and achieved a decent performance. Visit our poster at SciNLP!
Scientific endeavor revolves around scientists. Yet data about scientists are notoriously inaccurate due to the challenging and ubiquitous problem of author name disambiguation. Here, we propose a new disambiguation framework based on BERT, which can automatically select the most useful features for disambiguation and achieved a decent performance. Visit our poster at SciNLP!
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.
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.
Name disambiguation in Aminer
Zhang, Jing; Tang, Jie
Sci China Inf Sci, 2021, 64(4): 144101
Name disambiguation, aiming at disambiguating who is who, is one ...
Name disambiguation in Aminer
Zhang, Jing; Tang, Jie
Sci China Inf Sci, 2021, 64(4): 144101
Name disambiguation, aiming at disambiguating who is who, is one of the fundamental problems of the online academic network platforms such as Google scholar, microsoft academic and AMiner. This study takes AMiner, a free online academic search and mining system, as the example to explain how we deal with the name ambiguity problem under three different scenarios. AMiner has already extracted 13 million researchers' profiles from the Web and integrated with 20 million papers from heterogeneous publication databases, with a growth rate of over 500000 per month. From the beginning when the system is built to the running and updating phases, we need to pay continuous attention on the problem of name disambiguation. In the following parts, we discuss the problem on three scenarios during the whole life cycle of AMiner, i.e., name disambiguation when the system is built from scratch (full ND), name disambiguation when persons' profiles are continuously updated (continuous ND) and error detection upon existing persons' profiles (error detection).
Name disambiguation in Aminer
Zhang, Jing; Tang, Jie
Sci China Inf Sci, 2021, 64(4): 144101
Name disambiguation, aiming at disambiguating who is who, is one of the fundamental problems of the online academic network platforms such as Google scholar, microsoft academic and AMiner. This study takes AMiner, a free online academic search and mining system, as the example to explain how we deal with the name ambiguity problem under three different scenarios. AMiner has already extracted 13 million researchers' profiles from the Web and integrated with 20 million papers from heterogeneous publication databases, with a growth rate of over 500000 per month. From the beginning when the system is built to the running and updating phases, we need to pay continuous attention on the problem of name disambiguation. In the following parts, we discuss the problem on three scenarios during the whole life cycle of AMiner, i.e., name disambiguation when the system is built from scratch (full ND), name disambiguation when persons' profiles are continuously updated (continuous ND) and error detection upon existing persons' profiles (error detection).
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.
This video was recorded at Code BEAM America 2021 - https://codesync.global/conferences/code-beam-sf-2021/
And Yet Akin: Name Disambiguation in Elixir | Vanessa Lee - Senior Software Engineer at Interfolio
ABSTRACT
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.
OBJECTIVES:
To introduce the problem of name disambiguation and string comparison by looking at two existing string comparison libraries before addressing the process of combining them into a single repository. I hope attendees will leave understanding the problem as well as the strengths, limitations, and possibilities of the new library and how it can be used to address the challenges of name disambiguation.
AUDIENCE:
Beginner to intermediate programmers.
• Timecodes
00:00 - 03:54 - Intro
03:55 - 05:14 - String Comparison Algorithms
05:15 - 09:42 - Akin
09:43 - 13:20 - Axon & Training Data: DBLP
13:21 - 18:09 - NX and Axon
18:10 - 19:36 - What's next?
19:36 - 36:43 - QnA
• Follow us on social:
Twitter: https://twitter.com/CodeBEAMio
LinkedIn: https://www.linkedin.com/company/27159258
• Looking for a unique learning experience?
Attend the next Code Sync conference near you!
See what's coming up at: https://codesync.global
• SUBSCRIBE TO OUR CHANNEL
https://www.youtube.com/channel/UC47eUBNO8KBH_V8AfowOWOw
See what's coming up at: https://codesync.global
Authors:
Yutao Zhang (Tsinghua University); Fanjin Zhang (Tsinghua University); Peiran Yao (Tsinghua University); Jie Tang (Tsinghua University)
More on http://www.kdd.org/kdd2018/
Name disambiguation is a complex but highly relevant challenge whenever analysing real-world user data, such as data from version control systems. We propose gambit, a rule-based disambiguation tool that only relies on name and email information. We evaluate its performance against two commonly used algorithms with similar characteristics, on manually disambiguated ground-truth data from the Gnome GTK project. Our results show that gambit significantly outperforms both algorithms in terms of precision as well as F1 score.
Uploaded with Clowdr https://clowdr.org/
Scientific endeavor revolves around scientists. Yet data about scientists are notoriously inaccurate due to the challenging and ubiquitous problem of author name disambiguation. Here, we propose a new disambiguation framework based on BERT, which can automatically select the most useful features for disambiguation and achieved a decent performance. Visit our poster at SciNLP!
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.
Name disambiguation in Aminer
Zhang, Jing; Tang, Jie
Sci China Inf Sci, 2021, 64(4): 144101
Name disambiguation, aiming at disambiguating who is who, is one of the fundamental problems of the online academic network platforms such as Google scholar, microsoft academic and AMiner. This study takes AMiner, a free online academic search and mining system, as the example to explain how we deal with the name ambiguity problem under three different scenarios. AMiner has already extracted 13 million researchers' profiles from the Web and integrated with 20 million papers from heterogeneous publication databases, with a growth rate of over 500000 per month. From the beginning when the system is built to the running and updating phases, we need to pay continuous attention on the problem of name disambiguation. In the following parts, we discuss the problem on three scenarios during the whole life cycle of AMiner, i.e., name disambiguation when the system is built from scratch (full ND), name disambiguation when persons' profiles are continuously updated (continuous ND) and error detection upon existing persons' profiles (error detection).
"Disambiguate." ... In this week's "AustinAnswered" column, let's disambiguate the proper noun "Butler" as it applies to local place names ... "Butler" as a place name, on the other hand, derives from three prominent, but not closely related, Austin broods.
“Name change flying in adversity” (Page A1, April 14) ... The kerfuffle over local airport names might be resolved by a simple disambiguation identifying the locations where they are actually situated.
NamedEntityRecognition... Named Entity Recognition ... Importance of Named Entity Recognition ... NER is used to link named entities to their corresponding entities in knowledge bases, disambiguating between entities with the same name but different meanings.
Image. Robert Falconer/Netflix ...Photo ... Photo ... Photo ... Photo ... Now, just for the sake of disambiguation, this live-action series, which will adapt the Nickelodeon animated series of the same name, is distinct from Nick’s own efforts in the area of animation.
Of course, the easiest (and most correct) explanation of this is that Mysterio was a fraud, the line is just an Easter egg for fans, and it’s just a coincidence that he happened to pick the name of the main Marvel Comics universe.
... pronounced preference, and Elizabeth named her after herself. The need for disambiguation must have been constant, although Lissy’s registered pedigree name is WolfertonDrama.
... which the Queen had such pronounced preference, and Elizabeth named her after herself. The need for disambiguation must have been constant, although Lissy’s registered pedigree name is WolfertonDrama.