Unsupervised Adverbial Identification in Modern Chinese Literature

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
EditorsStefania Degaetano-Ortlieb, Anna Kazantseva, Nils Reiter, Stan Szpakowicz
PublisherAssociation for Computational Linguistics
Pages91-95
Number of pages5
ISBN (Print)9781954085916
Publication statusPublished - Nov 2021

Publication series

NameJoint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, LaTeCHCLfL - Co-located with the Conference on Empirical Methods in Natural Language Processing, EMNLP - Proceedings

Conference

Title5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCHCLfL 2021)
LocationVirtual
PlaceDominican Republic
CityPunta Cana
Period11 November 2021

Abstract

In many languages, adverbials can be derived from words of various parts-of-speech. In Chinese, the derivation may be marked either with the standard adverbial marker DI, or the non-standard marker DE. Since DE also serves double duty as the attributive marker, accurate identification of adverbials requires disambiguation of its syntactic role. As parsers are trained predominantly on texts using the standard adverbial marker DI, they often fail to recognize adverbials suffixed with the non-standard DE. This paper addresses this problem with an unsupervised, rule-based approach for adverbial identification that utilizes dependency tree patterns. Experiment results show that this approach outperforms a masked language model baseline. We apply this approach to analyze standard and non-standard adverbial marker usage in modern Chinese literature.

Citation Format(s)

Unsupervised Adverbial Identification in Modern Chinese Literature. / Xie, Wenxiu; Lee, John S. Y.; Zhan, Fangqiong et al.

Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. ed. / Stefania Degaetano-Ortlieb; Anna Kazantseva; Nils Reiter; Stan Szpakowicz. Association for Computational Linguistics, 2021. p. 91-95 (Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, LaTeCHCLfL - Co-located with the Conference on Empirical Methods in Natural Language Processing, EMNLP - Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review