Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding

Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, Juanzi Li


Abstract
Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative and quantitative analysis demonstrate the high quality of the word, entity and multi-prototype mention embeddings. Using entity linking as a study case, we apply our disambiguation method as well as the multi-prototype mention embeddings on the benchmark dataset, and achieve state-of-the-art performance.
Anthology ID:
P17-1149
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1623–1633
Language:
URL:
https://www.aclweb.org/anthology/P17-1149
DOI:
10.18653/v1/P17-1149
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1149.pdf