Bayesian Hierarchical Words Representation Learning

Oren Barkan; Idan Rejwan; Avi Caciularu; Noam Koenigstein

doi:https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf

Bayesian Hierarchical Words Representation Learning

Oren Barkan, Idan Rejwan, Avi Caciularu, Noam Koenigstein

מדעי המחשב

פרסום מחקרי: פרק בספר / בדוח / בכנס › פרסום בספר כנס › ביקורת עמיתים

תקציר

This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.

שפה מקורית	אנגלית אמריקאית
כותר פרסום המארח	ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
מוציא לאור	Association for Computational Linguistics (ACL)
עמודים	3871-3877
מספר עמודים	7
מסת"ב (אלקטרוני)	9781952148255
מזהי עצם דיגיטלי (DOIs)	https://doi.org/https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf
סטטוס פרסום	פורסם - 2020
אירוע	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, ארצות הברית משך הזמן: 5 יולי 2020 → 10 יולי 2020

סדרות פרסומים

שם	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (מודפס)	0736-587X

כנס

כנס	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
מדינה/אזור	ארצות הברית
עיר	Virtual, Online
תקופה	5/07/20 → 10/07/20

הערה ביבליוגרפית

Publisher Copyright:
© 2020 Association for Computational Linguistics

גישה למסמך

https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf

קבצים וקישורים אחרים

Link to publication in Scopus

פורמט ציטוט ביבליוגרפי

Barkan, O., Rejwan, I., Caciularu, A., & Koenigstein, N. (2020). Bayesian Hierarchical Words Representation Learning. ב- ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (עמודים 3871-3877). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL). https://doi.org/https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf

Barkan, Oren ; Rejwan, Idan ; Caciularu, Avi et al. / Bayesian Hierarchical Words Representation Learning. ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2020. עמודים 3871-3877 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

@inproceedings{5767519184e14edc80e03e4fbb291ad6,

title = "Bayesian Hierarchical Words Representation Learning",

abstract = "This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.",

author = "Oren Barkan and Idan Rejwan and Avi Caciularu and Noam Koenigstein",

note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics; 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 ; Conference date: 05-07-2020 Through 10-07-2020",

year = "2020",

doi = "https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf",

language = "American English",

series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics (ACL)",

pages = "3871--3877",

booktitle = "ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",

address = "United States",

}

Barkan, O, Rejwan, I, Caciularu, A & Koenigstein, N 2020, Bayesian Hierarchical Words Representation Learning. ב- ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), עמודים 3871-3877, 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Virtual, Online, ארצות הברית, 5/07/20. https://doi.org/https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf

Bayesian Hierarchical Words Representation Learning. / Barkan, Oren; Rejwan, Idan; Caciularu, Avi et al.
ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2020. עמוד 3871-3877 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

פרסום מחקרי: פרק בספר / בדוח / בכנס › פרסום בספר כנס › ביקורת עמיתים

TY - GEN

T1 - Bayesian Hierarchical Words Representation Learning

AU - Barkan, Oren

AU - Rejwan, Idan

AU - Caciularu, Avi

AU - Koenigstein, Noam

PY - 2020

Y1 - 2020

N2 - This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.

AB - This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.

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U2 - https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf

DO - https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf

M3 - Conference contribution

AN - SCOPUS:85092713013

T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics

SP - 3871

EP - 3877

BT - ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

PB - Association for Computational Linguistics (ACL)

T2 - 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

Y2 - 5 July 2020 through 10 July 2020

ER -

Barkan O, Rejwan I, Caciularu A, Koenigstein N. Bayesian Hierarchical Words Representation Learning. ב- ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2020. עמוד 3871-3877. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf

Bayesian Hierarchical Words Representation Learning

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