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New Technology of Library and Information Service  2014, Vol. 30 Issue (10): 42-48    DOI: 10.11925/infotech.1003-3513.2014.10.07
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On the Feasibility of Applying TimeML to the Annotation of Temporal Relations in Chinese Text
Li Lubiao, Zhang Junsheng, Zhang Yinsheng, Wang Huilin
Institute of Scientific & Technical Information of China, Beijing 100038, China
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[Objective] This paper carries on the research and experiment on the feasibility of applying TimeML to the annotation of temporal relations in Chinese text. [Methods] According to the characteristics of Chinese temporal expressions, this paper discusses the applicability of the main labels of TimeML in Chinese text based on TimeML and its main labels. [Results] Although there are some differences between Chinese and English in the grammatical structure and syntactic structure, the application of TimeML to the Chinese language is feasible. [Limitations] The main labels of TimeML can't be completely parallel implemented to the English-Chinese text on the grammar structure because of the differences of language structure between Chinese and English. [Conclusions] TimeML, a markup language of temporal relations in English text, can be effectively applied to the annotation of temporal relations in Chinese text. The study lays the foundation for the temporal ordering inference of events and further TRR research in Chinese text.

Key wordsTimeML      Chinese      Event      Temporal expression      TRR     
Received: 18 April 2014      Published: 28 November 2014
:  G355  

Cite this article:

Li Lubiao, Zhang Junsheng, Zhang Yinsheng, Wang Huilin. On the Feasibility of Applying TimeML to the Annotation of Temporal Relations in Chinese Text. New Technology of Library and Information Service, 2014, 30(10): 42-48.

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[1] 陈勇. 语言学研究中的标记理论[J]. 外语研究, 2002(6): 28-32. (Chen Yong. Markedness Theory in the Study of Linguistics [J]. Foreign Languages Research, 2002(6): 28-32.)
[2] Pustejovsky J, Castano J M, Ingria R, et al. TimeML: Robust Specification of Event and Temporal Expressions in Text[C]. In: Proceedings of the 5th International Workshop on Computational Semantics. 2003.
[3] Yoshikawa K, Riedel S, Asahara M, et al. Jointly Identifying Temporal Relations with Markov Logic [C]. In: Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009: 405-413.
[4] 王凤玲. 基于条件随机域模型的英语时间表达式识别研究[J]. 电子技术, 2012(5): 8-10. (Wang Fengling. English Temporal Expression Recognition Based on Conditional Random Fields [J]. Electronic Technology, 2012(5): 8-10.)
[5] Caselli T, dell'Orletta F, Prodanof I. TETI: A TimeML Compliant TimEx Tagger for Italian[C]. In: Proceedings of the 2009 International Multiconference on Computer Science and Information Technology. 2009: 185-192.
[6] Bittar A, Amsili P, Denis P, et al. French TimeBank: An ISO-TimeML Annotated Reference Corpus [C]. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. 2011: 130-134.
[7] Pustejovsky J, Hanks P, Sauri R, et al. The Timebank Corpus [C]. In: Proceedings of Corpus Linguistics. 2003: 647-656.
[8] Saurii R, Littman J, Knippen B, et al. TimeML[EB/OL]. [2014-03-22].
[9] Mani I, Schiffman B, Zhang J. Inferring Temporal Ordering of Events in News [C]. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. 2003: 55-57.
[10] Chambers N, Wang S, Jurafsky D. Classifying Temporal Relations Between Events [C]. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics. 2007: 173-176.
[11] Bethard S. ClearTK-TimeML: A Minimalist Approach to TempEval 2013 [C]. In: Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics. 2013: 10-14.
[12] Carpuat M. NRC: A Machine Translation Approach to Cross-Lingual Word Sense Disambiguation (SemEval-2013 Task 10)[C]. In: Proceedings of the 2nd Joint Conference on Lexical and Computational Semantics. 2013: 188-192.
[13] 李铁根. "了"、"着"、"过"与汉语时制的表达[J]. 语言研究, 2002(3): 1-13. (Li Tiegen. The Expression of "Le", "Zhe", "Guo" and Chinese Tense [J]. Sudies in Language and Linguistics, 2002(3): 1-13.)
[14] 李英哲, 郑良伟, Lorry Foster, 等. 实用汉语参考语法[M]. 熊文华译. 北京: 北京语言学院出版社, 1990. (Li Yingzhe, Zheng Liangwei, Lorry Foster, et al. Practical Chinese Reference Grammar [M]. Translated by Xiong Wenhua. Beijing: Beijing Language and Culture University Press, 1990.)
[15] Whorf B L. Science and Linguistics [J]. Technological Review, 1940: 42(6): 229-231.
[16] Linguistic Data Consortium. ACE (Automatic Concept Extraction) Chinese Event Guidelines V5.5.1[EB/OL]. (2005-07-01). [2014-03-22].
[17] Egges A, Nijholt A, Nugues P. CarSim: Automatic 3d Scene Generation of a Car Accident Description[R]. Technical Report, University of Twente, 2001.
[18] Boguraev B, Castaño J, Gaizauskas R, et al. TimeML 1.2.1: A Formal Specification Language for Events and Temporal Expressions [EB/OL]. [2014-03-22]. publications/timeMLdocs/timeml_1.2.1.html.
[19] 蔡维天. 谈汉语模态词的分布与诠释之对应关系[J]. 中国语文, 2010(3): 208-221. (Tsai W T. On the Syntax-Semantics Correspondences of Chinese Modals [J]. Studies of the Chinese Language, 2010(3): 208-221.)

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