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A Topic-Sensitive Model for Salient Entity Linking

Published: 2015 Oktober

Buchtitel: Proceedings of the LD4IE workshop co-located with the 14th International Semantic Web Conference (ISWC 2015)
Verlag: CEUR-WS
Referierte Veröffentlichung

In recent years, the amount of entities in large knowledge bases available on the Web has been increasing rapidly. Such entities can be used to bridge textual data with knowledge bases and thus help with many tasks, such as text understanding, word sense disambiguation and information retrieval. The key issue is to link the entity mentions in documents with the corresponding entities in knowledge bases, referred to as entity linking. In addition, for many entity-centric applications, entity salience for a document has become a very important factor. This raises an impending need to identify a set of salient entities that are central to the input document. In this paper, we introduce a new task of salient entity linking and propose a graph-based disambiguation solution, which integrates several features, especially a topic-sensitive model based on Wikipedia categories. Experimental results show that our method significantly outperforms the state-of-the-art entity linking methods in terms of precision, recall and F-measure.

Download: Media:Ld4ie01 Zhang.pdf