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Named Entity Recognition for Question Answering

by: Diego Mollá, Menno van Zaanen, and Daniel Smith

In: Proceedings ALTW 2006 (2006) , p. 51-58.

Abstract

Current text-based question answering QA systems usually contain a named entity recogniser NER as a core component. Named entity recognition as traditionally been developed as a component for information extraction systems, and current techniques are focused on this end use. However, no formal assessment has been done on the characteristics of a NER within the task of question answering. In this paper we present a NER that aims at higher recall by allowing multiple entity labels to strings. The NER is embedded in a question answering system and the overall QA system performance is compared to that of one with a traditional variation of the NER that only allows single entity labels. It is shown that the added noise produced introduced by the additional labels is offset by the higher recall gained, therefore enabling the QA system to have a better chance to find the answer.

BibTeX record

@inproceedings{Molla:2006a,
  abstract = {Current text-based question answering (QA) systems usually contain a named entity recogniser (NER) as a core component. Named entity recognition as traditionally been developed as a component for information extraction systems, and current techniques are focused on this end use. However, no formal assessment has been done on the characteristics of a NER within the task of question answering. In this paper we present a NER that aims at higher recall by allowing multiple entity labels to strings. The NER is embedded in a question answering system and the overall QA system performance is compared to that of one with a traditional variation of the NER that only allows single entity labels. It is shown that the added noise produced introduced by the additional labels is offset by the higher recall gained, therefore enabling the QA system to have a better chance to find the answer.},
  added-at = {2008-10-10T05:18:00.000+0200},
  author = {Moll{\'a}, Diego and van Zaanen, Menno and Smith, Daniel},
  biburl = {http://www.bibsonomy.org/bibtex/264e2ff61c23df0f5f02914ea091dc158/diego_ma},
  booktitle = {Proceedings ALTW 2006},
  interhash = {6759916d3748647a40af77f818a52130},
  intrahash = {64e2ff61c23df0f5f02914ea091dc158},
  keywords = {named_entities AnswerFinder molla_publication},
  pages = {51-58},
  timestamp = {2008-10-10T05:18:00.000+0200},
  title = {Named Entity Recognition for Question Answering},
  year = 2006
}

Endnote record

%0 Conference Paper
%1 Molla:2006a
%A Moll{\'a}, Diego
%A van Zaanen, Menno
%A Smith, Daniel
%B Proceedings ALTW 2006
%D 2006
%K 
%P 51-58
%T Named Entity Recognition for Question Answering
%X Current text-based question answering (QA) systems usually contain a named entity recogniser (NER) as a core component. Named entity recognition as traditionally been developed as a component for information extraction systems, and current techniques are focused on this end use. However, no formal assessment has been done on the characteristics of a NER within the task of question answering. In this paper we present a NER that aims at higher recall by allowing multiple entity labels to strings. The NER is embedded in a question answering system and the overall QA system performance is compared to that of one with a traditional variation of the NER that only allows single entity labels. It is shown that the added noise produced introduced by the additional labels is offset by the higher recall gained, therefore enabling the QA system to have a better chance to find the answer.