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Entity disambiguation with decomposable neural networks
Author(s) -
Sun Yaming,
Ji Zhenzhou,
Lin Lei,
Tang Duyu,
Wang Xiaolong
Publication year - 2017
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1215
Subject(s) - computer science , entity linking , artificial intelligence , convolutional neural network , task (project management) , benchmark (surveying) , natural language processing , named entity recognition , information retrieval , knowledge base , string (physics) , artificial neural network , physics , management , geodesy , quantum mechanics , economics , geography
Entity disambiguation is a fundamental task in natural language processing and computational linguistics. Given a query consisting of a mention (name string) and a background document, entity disambiguation aims at linking the mention to an entity from a reference knowledge base such as Wikipedia. A main challenge of this task is how to effectively represent the meaning of the mention and the entity, based on which the semantic relatedness between the mention and the entity could be conveniently measured. Towards this goal, we introduce computational models to effectively represent the mention and the entity in some vector space. We decompose the problem into subproblems and develop various neural network architectures, all of which are purely data‐driven and capable of learning continuous representations of the mention and the entity from data. To effectively train the neural network models, we explore a simple yet effective way that enables us to collect millions of training examples from Wikipedia without using any manual annotation. Empirical results on two benchmark datasets show that our approaches based on convolutional neural network and long short‐term memory consistently outperform top‐performed systems on both datasets. WIREs Data Mining Knowl Discov 2017, 7:e1215. doi: 10.1002/widm.1215 This article is categorized under: Algorithmic Development > Text Mining Algorithmic Development > Web Mining