DMCA
A generative entity-mention model for linking entities with knowledge base (2011)
| Venue: | In ACL |
| Citations: | 25 - 1 self |
BibTeX
@INPROCEEDINGS{Han11agenerative,
author = {Xianpei Han and Le Sun},
title = {A generative entity-mention model for linking entities with knowledge base},
booktitle = {In ACL},
year = {2011},
pages = {945--954}
}
Share
 |
 |
 |
 |
||
OpenURL
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. 1
Keyphrases
knowledge base generative entity-mention model name mention specific entity name variation problem possible context possible name name ambiguity problem key issue textual data three-step generative story traditional method entity knowledge heterogenous knowledge entity-mention model generative probabilistic model popularity knowledge referent entity name knowledge structural knowledge base heterogenous entity knowledge experimental result context knowledge
