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Title:
a generative entity-mention model for linking entities with knowledge base
Author: Han Xianpei ; Sun Le
Source: ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Conference Name: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
Conference Date: June 19, 2011 - June 24, 2011
Issued Date: 2011
Conference Place: Portland, OR, United states
Keyword: Computational linguistics ; Knowledge based systems
Indexed Type: EI
ISBN: 9781932432879
Department: (1) Institute of Software Chinese Academy of Sciences HaiDian District Beijing China
Sponsorship: Google; Baidu; Microsoft Research; Pacific Northwest National Laboratory; Yahoo
Abstract: Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entity-mention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods. © 2011 Association for Computational Linguistics.
English Abstract: Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entity-mention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods. © 2011 Association for Computational Linguistics.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16282
Appears in Collections:软件所图书馆_会议论文

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Han Xianpei,Sun Le. a generative entity-mention model for linking entities with knowledge base[C]. 见:49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011. Portland, OR, United states. June 19, 2011 - June 24, 2011.
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