标题:Relation Enhanced Neural Model for Type Classification of Entity Mentions with a Fine-Grained Taxonomy
作者:Cui, Kai-Yuan; Ren, Peng-Jie; Chen, Zhu-Min; Lian, Tao; Ma, Jun
作者机构:[Cui, K.-Y] Information Retrieval Laboratory, School of Computer Science and Technology, Shandong University, Jinan, 250101, China;[ Ren, P.-J] Inform 更多
通讯作者:Cui, Kai-Yuan(ckysdu@gmail.com)
通讯作者地址:[Cui, KY]Shandong Univ, Sch Comp Sci & Technol, Informat Retrieval Lab, Jinan 250101, Peoples R China.
来源:计算机科学技术学报(英文版)
出版年:2017
卷:32
期:4
页码:814-827
DOI:10.1007/s11390-017-1762-7
关键词:entity mention classification;entity mention relation;fine-grained taxonomy
摘要:Inferring semantic types of the entity mentions in a sentence is a necessary yet challenging task. Most of existing methods employ a very coarse-grained type taxonomy, which is too general and not exact enough for many tasks. However, the performances of the methods drop sharply when we extend the type taxonomy to a fine-grained one with several hundreds of types. In this paper, we introduce a hybrid neural network model for type classification of entity mentions with a fine-grained taxonomy. There are four components in our model, namely, the entity mention component, the context component, the relation component, the already known type component, which are used to extract features from the target entity mention, context, relations and already known types of the entity mentions in surrounding context respectively. The learned features by the four components are concatenated and fed into a softmax layer to predict the type distribution. We carried out extensive experiments to evaluate our proposed model. Experimental results demonstrate that our model achieves state-of-the-art performance on the FIGER dataset. Moreover, we extracted larger datasets from Wikipedia and DBpedia. On the larger datasets, our model achieves the comparable performance to the state-of-the-art methods with the coarse-grained type taxonomy, but performs much better than those methods with the fine-grained type taxonomy in terms of micro-F 1, macro-F 1 and weighted-F 1.
收录类别:EI;CSCD;SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024123340&doi=10.1007%2fs11390-017-1762-7&partnerID=40&md5=4cfe51ff4851d71dcbd1870b8bcb22c1
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