Mining cross-cultural differences and similarities in social media

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

doi:10.18653/v1/p18-1066

Mining cross-cultural differences and similarities in social media

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

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 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 language	English
Title of host publication	ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Publisher	Association for Computational Linguistics (ACL)
Pages	709-719
Number of pages	11
ISBN (Electronic)	9781948087322
DOIs	https://doi.org/10.18653/v1/p18-1066
Publication status	Published - 2018
Event	56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia Duration: 2018 Jul 15 → 2018 Jul 20

Publication series

Name	ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume	1

Conference

Conference	56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
Country/Territory	Australia
City	Melbourne
Period	18/7/15 → 18/7/20

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

Access to Document

10.18653/v1/p18-1066

Cite this

Lin, B. Y., Xu, F. F., Zhu, K. Q., & Hwang, S. W. (2018). Mining cross-cultural differences and similarities in social media. In ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) (pp. 709-719). (ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers); Vol. 1). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-1066

Lin, Bill Yuchen ; Xu, Frank F. ; Zhu, Kenny Q. et al. / Mining cross-cultural differences and similarities in social media. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Association for Computational Linguistics (ACL), 2018. pp. 709-719 (ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)).

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title = "Mining cross-cultural differences and similarities in social media",

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.",

author = "Lin, {Bill Yuchen} and Xu, {Frank F.} and Zhu, {Kenny Q.} and Hwang, {Seung Won}",

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: {\textcopyright} 2018 Association for Computational Linguistics; 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 ; Conference date: 15-07-2018 Through 20-07-2018",

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doi = "10.18653/v1/p18-1066",

language = "English",

series = "ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)",

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Lin, BY, Xu, FF, Zhu, KQ & Hwang, SW 2018, Mining cross-cultural differences and similarities in social media. in ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), vol. 1, Association for Computational Linguistics (ACL), pp. 709-719, 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 18/7/15. https://doi.org/10.18653/v1/p18-1066

Mining cross-cultural differences and similarities in social media. / Lin, Bill Yuchen; Xu, Frank F.; Zhu, Kenny Q. et al.
ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Association for Computational Linguistics (ACL), 2018. p. 709-719 (ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers); Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - 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

PY - 2018

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N2 - 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.

AB - 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.

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Lin BY, Xu FF, Zhu KQ, Hwang SW. Mining cross-cultural differences and similarities in social media. In ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Association for Computational Linguistics (ACL). 2018. p. 709-719. (ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)). doi: 10.18653/v1/p18-1066

Mining cross-cultural differences and similarities in social media

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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