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A neural network framework for relation extraction: Learning entity semantic and relation pattern
Jan 03, 2017Author：
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Title: A neural network framework for relation extraction: Learning entity semantic and relation pattern
Authors: Zheng, SC; Xu, JM; Zhou, P; Bao, HY; Qi, ZY; Xu, B
Author Full Names: Zheng, Suncong; Xu, Jiaming; Zhou, Peng; Bao, Hongyun; Qi, Zhenyu; Xu, Bo
Source: KNOWLEDGE-BASED SYSTEMS, 114 12-23; 10.1016/j.knosys.2016.09.019 DEC 15 2016
Language: English
Abstract: Relation extraction is to identify the relationship of two given entities in the text. It is an important step in the task of knowledge extraction. Most conventional methods for the task of relation extraction focus on designing effective handcrafted features or learning a semantic representation of the whole sentence. Sentences with the same relationship always share the similar expressions. Besides, the semantic properties of given entities can also help to distinguish some confusing relations. Based on the above observations, we propose a neural network based framework for relation classification. It can simultaneously learn the relation pattern's information and the semantic properties of given entities. In this framework, we explore two specific models: the CNN-based model and LSTM-based model. We conduct experiments on two public datasets: the SemEval-2010 Task8 dataset and the ACE05 dataset. The proposed method achieves the state-of-the-art result without using any external information. Additionally, the experimental results also show that our approach can represent the semantic relationship of the given entities effectively. (C) 2016 Elsevier B.V. All rights reserved.
ISSN: 0950-7051
eISSN: 1872-7904
IDS Number: EE2EP
Unique ID: WOS:000389396800002
 

 

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