| a generative entity-mention model for linking entities with knowledge base |
| Han Xianpei; Sun Le
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| 2011
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| 会议名称 | 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
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| 会议录名称 | ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
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| 页码 | 945-954
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| 会议日期 | June 19, 2011 - June 24, 2011
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| 会议地点 | Portland, OR, United states
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| 收录类别 | EI
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| ISBN | 9781932432879
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| 部门归属 | (1) Institute of Software Chinese Academy of Sciences HaiDian District Beijing China
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| 摘要 | 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.; 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. |
| 关键词 | Computational Linguistics
Knowledge Based Systems
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| 主办者 | Google; Baidu; Microsoft Research; Pacific Northwest National Laboratory; Yahoo
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| 语种 | 英语
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| 内容类型 | 会议论文
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| URI标识 | http://ir.iscas.ac.cn/handle/311060/16282
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| 专题 | 中国科学院软件研究所
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推荐引用方式
GB/T 7714 |
Han Xianpei,Sun Le. a generative entity-mention model for linking entities with knowledge base[C],2011:945-954.
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