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Entity Linking Leveraging Automatically Generated Annotation
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BibTeX
@MISC{Zhang_entitylinking,
author = {Wei Zhang and Jian Su and Chew Lim and Tan Wen and Ting Wang},
title = {Entity Linking Leveraging Automatically Generated Annotation},
year = {}
}
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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. 1
Keyphrases
document collection state-of-the-art approach current mention large scale corpus annotation entity linking additional information source knowledge base kb entity experiment result wikipedia document new entity name variation generated corpus unambiguous synonym performance improvement ambiguous mention refers entity mention domain adaption approach novel method binary classifier training corpus additional information
