Accession Number:

ADA517741

Title:

Entity Retrieval by Hierarchical Relevance Model, Exploiting the Structure of Tables and Learning Homepage Classifiers

Descriptive Note:

Conference paper

Corporate Author:

PURDUE UNIV LAFAYETTE IN DEPT OF COMPUTER SCIENCES

Report Date:

2009-11-01

Pagination or Media Count:

7.0

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 years 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.

Subject Categories:

  • Information Science
  • Computer Programming and Software
  • Test Facilities, Equipment and Methods
  • Linguistics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE