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Extracting answers to natural language questions from large-scale corpus

  • Peng Li*
  • , Xiao Long Wang
  • , Yi Guan
  • , Yu Ming Zhao
  • *Corresponding author for this work
  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper provides a novel and tractable method for extracting exact textual answers from the returned documents that are retrieved by traditional IR system in large-scale collection of texts. In our approach, WordNet and Web information are employed to improve the performance as external auxiliary resources, then some NLP technologies are used to constitute the empirical answer ranking formula, such as POS tagging, Named Entity recognition, and parser etc. The method involves automatically ranking passages with System Similarity Model, automatically downloading related Web pages by means of Web crawler, and automatically mining answers with empirical formula from candidate answer sets. The series of experimental results show that the overall performance of our system is good and the structure of the system is reasonable.

Original languageEnglish
Title of host publicationProceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05
Pages690-694
Number of pages5
DOIs
StatePublished - 2005
Externally publishedYes
Event2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05 - Wuhan, China
Duration: 30 Oct 20051 Nov 2005

Publication series

NameProceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05
Volume2005

Conference

Conference2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE'05
Country/TerritoryChina
CityWuhan
Period30/10/051/11/05

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