ICTNET at Entity Track TREC 2010

Abstract

This paper gives an overview of our work for related entity finding which is proposed in TREC 2010 Entity Track. The goal of the Entity Track is to find the entities relevant to a given query from the web corpus. In this paper, we propose a bipartite graph reinforcement model for entity ranking. As is well known, the entities on the web are embedded not only in the natural language text, but also in the tables and lists. Given a query, both the candidate entities and relevant tables/lists are extracted from web documents. Then the candidate entities extracted from unstructured text are ranked based on a probabilistic model. But the result contains a lot of noise. If some candidate entities are in a relevant table/list, they are more relevant to the given query. And Vice versa, if a table/list contains several candidate entities, it is also more relevant to the query. Based on the above intuition, we construct a bipartite graph and then perform a reinforcement algorithm to re-rank the candidate entities.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2010
Accession Number
ADA546578

Entities

People

  • Hongbo Xu
  • Jiafeng Guo
  • Lei Cao
  • Lu Bai
  • Xiaoming Yu
  • Xueqi Cheng
  • Yue Liu

Organizations

  • Chinese Academy of Sciences

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Heuristic Methods
  • Information Operations
  • Information Retrieval
  • Language
  • Mathematical Models
  • Mathematics
  • Models
  • Natural Languages
  • Probabilistic Models
  • Probability
  • Reinforcement Learning
  • Standards
  • World Wide Web

Fields of Study

  • Computer science

Readers

  • Graph Algorithms and Convex Optimization.
  • Information Retrieval
  • Neural Network Machine Learning.