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

نتاج البحث: فصل من :كتاب / تقرير / مؤتمر › منشور من مؤتمر › مراجعة النظراء

ملخص

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.

اللغة الأصلية	إنجليزيّة أمريكيّة
عنوان منشور المضيف	ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
ناشر	Association for Computational Linguistics (ACL)
الصفحات	3871-3877
عدد الصفحات	7
رقم المعيار الدولي للكتب (الإلكتروني)	9781952148255
المعرِّفات الرقمية للأشياء	https://doi.org/https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf
حالة النشر	نُشِر - 2020
الحدث	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, الولايات المتّحدة المدة: ٥ يوليو ٢٠٢٠ → ١٠ يوليو ٢٠٢٠

سلسلة المنشورات

الاسم	Proceedings of the Annual Meeting of the Association for Computational Linguistics
رقم المعيار الدولي للدوريات (المطبوع)	0736-587X

!!Conference

!!Conference	58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
الدولة/الإقليم	الولايات المتّحدة
المدينة	Virtual, Online
المدة	٥/٠٧/٢٠ → ١٠/٠٧/٢٠

ملاحظة ببليوغرافية

Publisher Copyright:
© 2020 Association for Computational Linguistics

الوصول إلى المستند

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

الملفات والروابط الأخرى

Link to publication in Scopus

قم بذكر هذا

Barkan, O., Rejwan, I., Caciularu, A., & Koenigstein, N. (2020). Bayesian Hierarchical Words Representation Learning. في ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (الصفحات 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 وآخرون. / 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. الصفحات 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. في 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), الصفحات 3871-3877, 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Virtual, Online, الولايات المتّحدة, ٥/٠٧/٢٠. https://doi.org/https://arxiv.org/ftp/arxiv/papers/2004/2004.07126.pdf

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

نتاج البحث: فصل من :كتاب / تقرير / مؤتمر › منشور من مؤتمر › مراجعة النظراء

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.

UR - http://www.scopus.com/inward/record.url?scp=85092713013&partnerID=8YFLogxK

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. في ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2020. صفحة 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

ملخص

سلسلة المنشورات

!!Conference

ملاحظة ببليوغرافية

الوصول إلى المستند

الملفات والروابط الأخرى

بصمة

قم بذكر هذا