Web Person Name Disambiguation by Relevance Weighting of Extended Feature Sets

This paper describes our approach to the Person Name Disambiguation clustering task in the Third Web People Search Evaluation Campaign(WePS3). The method focuses on two aspects: the extended feature sets, and feature relevance weighting. Bag-of-words and named entities are most commonly used features in many existing web entity disambiguation algorithms and we further extend this basic feature set with Wikipedia concepts. Then two feature weighting models are employed. One is the feature relevance to the target person name(or “query name”), and the other is the feature relevance to the text content. Similarity score is calculated according to the feature weights for clustering documents of the same person. Experiments show that the system based on our approach has generated the best results among all the WePS-3’s submissions.

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