@inproceedings{xie-etal-2021-unsupervised,
title = "Unsupervised Adverbial Identification in {M}odern {C}hinese Literature",
author = "Xie, Wenxiu and
Lee, John and
Zhan, Fangqiong and
Han, Xiao and
Chow, Chi-Yin",
editor = "Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.latechclfl-1.10",
doi = "10.18653/v1/2021.latechclfl-1.10",
pages = "91--95",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Unsupervised Adverbial Identification in Modern Chinese Literature
%A Xie, Wenxiu
%A Lee, John
%A Zhan, Fangqiong
%A Han, Xiao
%A Chow, Chi-Yin
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic (online)
%F xie-etal-2021-unsupervised
%X 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.
%R 10.18653/v1/2021.latechclfl-1.10
%U https://aclanthology.org/2021.latechclfl-1.10
%U https://doi.org/10.18653/v1/2021.latechclfl-1.10
%P 91-95
Markdown (Informal)
[Unsupervised Adverbial Identification in Modern Chinese Literature](https://aclanthology.org/2021.latechclfl-1.10) (Xie et al., LaTeCHCLfL 2021)
ACL
- Wenxiu Xie, John Lee, Fangqiong Zhan, Xiao Han, and Chi-Yin Chow. 2021. Unsupervised Adverbial Identification in Modern Chinese Literature. In Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 91–95, Punta Cana, Dominican Republic (online). Association for Computational Linguistics.