Named Entity Disambiguation: a Hybrid Approach

Abstract Semantic annotation of named entities for enriching unstructured content is a critical step in development of Semantic Web and many Natural Language Processing applications. To this end, this paper addresses the named entity disambiguation problem that aims at detecting entity mentions in a text and then linking them to entries in a knowledge base. In this paper, we propose a hybrid method, combining heuristics and statistics, for named entity disambiguation. The novelty is that the disambiguation process is incremental and includes several rounds that filter the candidate referents, by exploiting previously identified entities and extending the text by those entity attributes every time they are successfully resolved in a round. Experiments are conducted to evaluate and show the advantages of the proposed method. The experiment results show that our approach achieves high accuracy and can be used to construct a robust entity disambiguation system.

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