Mining cross-cultural differences and similarities in social media

Bill Yuchen Lin, Frank F. Xu, Kenny Q. Zhu, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media. For instance, people of distinct cultures often hold different opinions on a single named entity. Also, understanding slang terms across languages requires knowledge of cross-cultural similarities. In this paper, we study the problem of computing such cross-cultural differences and similarities. We present a lightweight yet effective approach, and evaluate it on two novel tasks: 1) mining cross-cultural differences of named entities and 2) finding similar terms for slang across languages. Experimental results show that our framework substantially outperforms a number of baseline methods on both tasks. The framework could be useful for machine translation applications and research in computational social science.

Original languageEnglish
Title of host publicationACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
PublisherAssociation for Computational Linguistics (ACL)
Pages709-719
Number of pages11
ISBN (Electronic)9781948087322
DOIs
Publication statusPublished - 2018
Event56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia
Duration: 2018 Jul 152018 Jul 20

Publication series

NameACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume1

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
CountryAustralia
CityMelbourne
Period18/7/1518/7/20

Bibliographical note

Funding Information:
Kenny Zhu is the contact author and was supported by NSFC grants 91646205 and 61373031. Seung-won Hwang was supported by Microsoft Research Asia. Thanks to the anonymous reviewers and Hanyuan Shi for their valuable feedback.

Publisher Copyright:
© 2018 Association for Computational Linguistics

All Science Journal Classification (ASJC) codes

  • Software
  • Computational Theory and Mathematics

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