事件抽取旨在把含有事件信息的非结构化文本以结构化的形式予以呈现。现有的基于监督学习的事件抽取方法往往受限于数据稀疏和分布不平衡问题,具有较低的召回率。针对这一问题,该文提出一种利用框架语义优化事件抽取的方法,引入框架类型作为泛化特征,在此基础上进行框架类型和事件类型的映射,然后结合框架类型识别模型和事件类型识别模型进行协作判定,以此优化事件抽取的召回性能。实验结果显示,针对触发词(事件类型)识别任务,相较于仅使用事件类型识别模型,该文提出的框架语义辅助的事件类型识别模型能够提高抽取召回率6.44%(5.74%),提高F值1.45%(0.83%)。
Abstract
Event extraction aims at detecting certain specified types of events that are mentioned in the source language data. Existing methods based on supervised learning often suffer from date sparseness and imbalanced distribution, producing low recall as a reuslt. In this paper, we investigate the frame semantic knowledge to improve event extraction. Taking the frame type as general feature and mapping the frames into events, we combine the event recognition model with the frame recognition model for a joint decision. Compared to the previous event recognition model, experiments show that this method achieves 6.44%(5.74%) gain in recall and 1.45%(0.83%) gain in F1 for the task of trigger (event) identification.
关键词
事件抽取 /
信息抽取 /
框架语义
{{custom_keyword}} /
Key words
event extraction /
information extraction /
frame semantic
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Collin F. Baker, Charles J. Fillmore, John B. Lowe.The berkeley framenet project [C]//Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics(ACL), Montreal, Canada, 1998, 1: 86-90.
[2] George A. Miller. WordNet: a lexical database for English [J]. Communications of the ACM, 1995, 38(11): 39-41.
[3] Zhendong Dong, Qiang Dong. HowNet and the Computation of Meaning [M]. Singapore, World Scientific, 2006.
[4] Ludovic Denoyer, Patrick Gallinari. The wikipedia xml corpus[J]. Comparative Evaluation of XML Information Retrieval Systems, 2007, 4518: 12-19.
[5] Ralph Grishman, David Westbrook, Adam Meyers. NYUs English ACE 2005 System Description[C]//Proceedings of ACE 2005 Evaluation Workshop, Gaithersburg, USA, 2005: 5-19.
[6] David Ahn. The stages of event extraction[C]//Proceedings of ACL 2006 Workshop on Annotating and Reasoning about Time and Events, Sydney, Australia, 2006: 1-8.
[7] Zhen Chen, Heng Ji. Language specific issue and feature exploration in Chinese event extraction[C]//Proceedings of the 2009 North American Chapter of the Association for Computational Linguistics(NAACL), Blouder, Colorado, 2009, Short Papers, 1: 209-212.
[8] Peifeng Li, Guodong Zhou. Employing Morphological Structures and Sememes for Chinese Event Extraction[C]//Proceedings of COLING 2012, Mumbai, India, 2012: 1619-1634
[9] Chen Chen, Vincent Ng. Joint modeling for Chinese event extraction with rich linguistic features[C]//Proceedings of COLING 2012, Mumbai, India, 2012: 529-544
[10] Qi Li, Heng Ji, Liang Huang. Joint Event Extraction via Structured Prediction with Global Features[C]//Proceedings of the 51th Annual Meeting of the Association for Computational Linguistics(ACL). Sofia, Bulgaria, 2013: 73-82.
[11] Heng Ji, Ralph Grishman. Refining Event Extraction through Cross-Document Inference[C]//Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics(ACL), Colunbus, USA, 2008: 254-262.
[12] Shasha Liao, Ralph Grishman. Using Document Level Cross-Event Inference to Improve Event Extraction[C]//Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics(ACL), Uppsala, Sweden, 2010: 789-797.
[13] Yu Hong, Jianfeng Zhang, Bin Ma, et al. Using cross-entity inference to improve event extraction[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics(ACL), Portland, USA, 2010, 1: 1127-1136.
[14] Lingling Meng, Runqing Huang, Junzhong Gu. A review of semantic similarity measures in wordnet[J]. International Journal of Hybrid Information Technology, 2013, 6(1): 1-12.
[15] Scott Miller, Jethran Guinness, Alex Zamanian. Name tagging with word clusters and discriminative training[C]//Proceedings of the 2004 North American Chapter of the Association for Computational Linguistics(NAACL), Boston, USA, 2004, 4: 337-342.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金(61373097, 61272259, 61272260)
{{custom_fund}}
PDF(2621 KB)
