Please use this identifier to cite or link to this item:
Title: Entity linking leveraging automatically generated annotation
Authors: Zhang, W.
Su, J.
Tan, C.L. 
Wang, W.T.
Issue Date: 2010
Citation: Zhang, W.,Su, J.,Tan, C.L.,Wang, W.T. (2010). Entity linking leveraging automatically generated annotation. Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference 2 : 1290-1298. ScholarBank@NUS Repository.
Abstract: Entity linking refers entity mentions in a document to their representations in a knowledge base (KB). In this paper, we propose to use additional information sources from Wikipedia to find more name variations for entity linking task. In addition, as manually creating a training corpus for entity linking is laborintensive and costly, we present a novel method to automatically generate a large scale corpus annotation for ambiguous mentions leveraging on their unambiguous synonyms in the document collection. Then, a binary classifier is trained to filter out KB entities that are not similar to current mentions. This classifier not only can effectively reduce the ambiguities to the existing entities in KB, but also be very useful to highlight the new entities to KB for the further population. Furthermore, we also leverage on the Wikipedia documents to provide additional information which is not available in our generated corpus through a domain adaption approach which provides further performance improvements. The experiment results show that our proposed method outperforms the state-of-the-art approaches.
Source Title: Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Apr 19, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.