Entity retrieval by hierarchical relevance model, exploiting the structure of tables and learning homepage classifiers

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Entity retrieval by hierarchical relevance model, exploiting the structure of tables and learning homepage classifiers is a 2009 conference paper written in English by Fang Y., Si L., Yu Z., Xian Y., Xu Y. and published in NIST Special Publication.

[edit] Abstract

This paper gives an overview of our work done for the TREC 2009 Entity track. We propose a hierarchical relevance retrieval model for entity ranking. In this model, three levels of relevance are examined which are document, passage and entity, respectively. The final ranking score is a linear combination of the relevance scores from the three levels. Furthermore, we exploit the structure of tables and lists to identify the target entities from them by making a joint decision on all the entities with the same attribute. To find entity homepages, we train logistic regression models for each type of entities. A set of templates and filtering rules are also used to identify target entities. The key lessons that we learned by participating this year's Entity track include: 1) our special treatment of table and list data is well rewarding; 2) The high accuracy of homepage finding is crucial in this track; 3) Wikipedia can serve as a valuable knowledge resource for different aspects of the related entity finding task.

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