PKU-HIT: An event detection system based on instances expansion and rich syntactic features

Shiqi Li; Pengyuan Liu; Tiejun Zhao; Qin Lu; Hanjing Li

PKU-HIT: An event detection system based on instances expansion and rich syntactic features

Shiqi Li, Pengyuan Liu, Tiejun Zhao, Qin Lu, Hanjing Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

This paper describes the PKU-HIT system on event detection in the SemEval-2010 Task. We construct three modules for the three sub-tasks of this evaluation. For target verb WSD, we build a Naïve Bayesian classifier which uses additional training instances expanded from an untagged Chinese corpus automatically. For sentence SRL and event detection, we use a feature-based machine learning method which makes combined use of both constituent-based and dependencybased features. Experimental results show that the Macro Accuracy of the WSD module reaches 83.81% and F-Score of the SRL module is 55.71%.

Original language	English
Title of host publication	ACL 2010 - SemEval 2010 - 5th International Workshop on Semantic Evaluation, Proceedings
Publisher	Association for Computational Linguistics (ACL)
Pages	304-307
Number of pages	4
ISBN (Electronic)	1932432701, 9781932432701
Publication status	Published - 1 Jan 2010
Event	5th International Workshop on Semantic Evaluation, SemEval 2010 - Uppsala University, Uppsala, Sweden Duration: 15 Jul 2010 → 16 Jul 2010

Conference

Conference	5th International Workshop on Semantic Evaluation, SemEval 2010
Country	Sweden
City	Uppsala
Period	15/07/10 → 16/07/10

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Theoretical Computer Science

More information

Scopus Link

Cite this

@inproceedings{869ee7d0901a457b832310d23673e5ba,

title = "PKU-HIT: An event detection system based on instances expansion and rich syntactic features",

abstract = "This paper describes the PKU-HIT system on event detection in the SemEval-2010 Task. We construct three modules for the three sub-tasks of this evaluation. For target verb WSD, we build a Na{\"i}ve Bayesian classifier which uses additional training instances expanded from an untagged Chinese corpus automatically. For sentence SRL and event detection, we use a feature-based machine learning method which makes combined use of both constituent-based and dependencybased features. Experimental results show that the Macro Accuracy of the WSD module reaches 83.81% and F-Score of the SRL module is 55.71%.",

author = "Shiqi Li and Pengyuan Liu and Tiejun Zhao and Qin Lu and Hanjing Li",

year = "2010",

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booktitle = "ACL 2010 - SemEval 2010 - 5th International Workshop on Semantic Evaluation, Proceedings",

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Li, S, Liu, P, Zhao, T, Lu, Q & Li, H 2010, PKU-HIT: An event detection system based on instances expansion and rich syntactic features. in ACL 2010 - SemEval 2010 - 5th International Workshop on Semantic Evaluation, Proceedings. Association for Computational Linguistics (ACL), pp. 304-307, 5th International Workshop on Semantic Evaluation, SemEval 2010, Uppsala, Sweden, 15/07/10.

PKU-HIT: An event detection system based on instances expansion and rich syntactic features. / Li, Shiqi; Liu, Pengyuan; Zhao, Tiejun; Lu, Qin; Li, Hanjing.

ACL 2010 - SemEval 2010 - 5th International Workshop on Semantic Evaluation, Proceedings. Association for Computational Linguistics (ACL), 2010. p. 304-307.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - This paper describes the PKU-HIT system on event detection in the SemEval-2010 Task. We construct three modules for the three sub-tasks of this evaluation. For target verb WSD, we build a Naïve Bayesian classifier which uses additional training instances expanded from an untagged Chinese corpus automatically. For sentence SRL and event detection, we use a feature-based machine learning method which makes combined use of both constituent-based and dependencybased features. Experimental results show that the Macro Accuracy of the WSD module reaches 83.81% and F-Score of the SRL module is 55.71%.

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