Question answering using maximum entropy components

  • Authors:
  • Abraham Ittycheriah;Martin Franz;Wei-Jing Zhu;Adwait Ratnaparkhi;Richard J. Mammone

  • Affiliations:
  • Yorktown Heights, NY;Yorktown Heights, NY;Yorktown Heights, NY;Yorktown Heights, NY;Rutgers University, Piscataway, NJ

  • Venue:
  • NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
  • Year:
  • 2001

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Abstract

We present a statistical question answering system developed for TREC-9 in detail. The system is an application of maximum entropy classification for question/answer type prediction and named entity marking. We describe our system for information retrieval which did document retrieval from a local encyclopedia, and then expanded the query words and finally did passage retrieval from the TREC collection. We will also discuss the answer selection algorithm which determines the best sentence given both the question and the occurrence of a phrase belonging to the answer class desired by the question. A new method of analyzing system performance via a transition matrix is shown.