a generative entity-mention model for linking entities with knowledge base
文献类型：会议论文
| 作者 | Han Xianpei ; Sun Le |
| 出版日期 | 2011 |
| 会议名称 | 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 |
| 会议日期 | June 19, 2011 - June 24, 2011 |
| 会议地点 | Portland, OR, United states |
| 关键词 | Computational linguistics Knowledge based systems |
| 页码 | 945-954 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | Google; Baidu; Microsoft Research; Pacific Northwest National Laboratory; Yahoo |
| 会议录 | ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
 |
| 语种 | 英语 |
| ISBN号 | 9781932432879 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16282]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 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]. 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. |
入库方式： OAI收割
来源：软件研究所
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