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Developing position structure based framework for Chinese entity relation extraction

Zhang, Peng; Li, Wenjie; Hou, Yuexian and Song, Dawei (2011). Developing position structure based framework for Chinese entity relation extraction. ACM Transactions on Asian Language Information Processing, 10(3) 14/1-14/22.

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Relation extraction is the task of finding semantic relations between two entities in text, and is often cast as a classification problem. In contrast to the significant achievements on English language, research progress in Chinese relation extraction is relatively limited. In this article, we present a novel Chinese relation extraction framework, which is mainly based on a 9-position structure. The design of this proposed structure is motivated by the fact that there are some obvious connections between relation types/subtypes and position structures of two entities. The 9-position structure can be captured with less effort than applying deep natural language processing, and is effective to relieve the class imbalance problem which often hurts the classification performance. In our framework, all involved features do not require Chinese word segmentation, which has long been limiting the performance of Chinese language processing. We also utilize some correction and inference mechanisms to further improve the classified results. Experiments on the ACE 2005 Chinese data set show that the 9-position structure feature can provide strong support for Chinese relation extraction. As well as this, other strategies are also effective to further improve the performance.

Item Type: Journal Item
Copyright Holders: 2011 ACM
ISSN: 1558-3430
Keywords: entity relation extraction; Chinese language; position structure; imbalance class classification
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 33894
Depositing User: Dawei Song
Date Deposited: 21 Jun 2012 08:44
Last Modified: 07 Dec 2018 10:06
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