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

6 Citations (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
Country/TerritoryAustralia
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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