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| a generative entity-mention model for linking entities with knowledge base | |
| Han Xianpei; Sun Le | |
| 2011 | |
| Conference Name | 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 |
| Source | ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies |
| Pages | 945-954 |
| Conference Date | June 19, 2011 - June 24, 2011 |
| Conference Place | Portland, OR, United states |
| Indexed Type | EI |
| ISBN | 9781932432879 |
| Department | (1) Institute of Software Chinese Academy of Sciences HaiDian District Beijing China |
| 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.; 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. |
| Keyword | Computational Linguistics Knowledge Based Systems |
| Sponsorship | Google; Baidu; Microsoft Research; Pacific Northwest National Laboratory; Yahoo |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16282 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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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