Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

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.

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017 July 30 - August 4

First Page

1623

Last Page

1633

Identifier

10.18653/v1/P17-1149

Publisher

Association for Computational Linguistics

City or Country

Vancouver, Canada

Additional URL

http://doi.org/10.18653/v1/P17-1149

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