2011 AGenerativeEntityMentionModelfo

• (Han et al., 2011) ⇒ Xianpei Han, and Le Sun. (2011). “A Generative Entity-mention Model for Linking Entities with Knowledge Base.” In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1.

Subject Headings: Entity Mention Normalization Task.

Cited By

• http://scholar.google.com/scholar?q=%22A+generative+entity-mention+model+for+linking+entities+with+knowledge+base%22+2011
• http://dl.acm.org/citation.cfm?id=2002472.2002592&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 AGenerativeEntityMentionModelfo	Xianpei Han Le Sun			A Generative Entity-mention Model for Linking Entities with Knowledge Base