Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2015

Abstract

Target entity disambiguation (TED), the task of identifying target entities of the same domain, has been recognized as a critical step in various important applications. In this paper, we propose a graphbased model called TremenRank to collectively identify target entities in short texts given a name list only. TremenRank propagates trust within the graph, allowing for an arbitrary number of target entities and texts using inverted index technology. Furthermore, we design a multi-layer directed graph to assign different trust levels to short texts for better performance. The experimental results demonstrate that our model outperforms state-of-the-art methods with an average gain of 24.8% in accuracy and 15.2% in the F1-measure on three datasets in different domains.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, September17-21

First Page

654

Last Page

664

Identifier

10.18653/v1/D15-1077

Publisher

Association for Computational Linguistics

City or Country

Lisbon, Portugal

Additional URL

http://doi.org/10.18653/v1/D15-1077

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