Multilingual dependency learning: A huge feature engineering method to semantic dependency parsing

被引：0

作者：

Zhao, Hai ^{[1
,3
]}

Chen, Wenliang ^{[2
]}

Kit, Chunyu ^{[1
]}

Zhou, Guodong ^{[3
]}

机构：

[1] Department of Chinese, Translation and Linguistics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, Hong Kong

[2] Language Infrastructure Group, MASTAR Project, National Institute of Information and Communications Technology, 3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan

[3] School of Computer Science and Technology, Soochow University, Suzhou 215006, China

来源：

CoNLL- 2009: Shared Task - Proceedings of the Thirteenth Conference on Computational Natural Language Learning, CoNLL: Shared Task | 2009年

关键词：

Computational linguistics - Natural language processing systems - Learning systems;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper describes our system about multilingual semantic dependency parsing (SRLonly) for our participation in the shared task of CoNLL-2009. We illustrate that semantic dependency parsing can be transformed into a word-pair classification problem and implemented as a single-stage machine learning system. For each input corpus, a large scale feature engineering is conducted to select the best fit feature template set incorporated with a proper argument pruning strategy. The system achieved the top average score in the closed challenge: 80.47% semantic labeled F1 for the average score. © 2009 Association for Computational Linguistics.

引用

页码：55 / 60

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Department of Chinese, Translation and Linguistics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong
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CoNLL- 2009: Shared Task - Proc. Thirteenth Conf. Comput. Natural Lang. Learn., CoNLL: Shared Task, 2009, (61-66):
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