Analysing definition questions by two machine learning approaches

In automatic question answering, the identification of the correct target term (i.e. the term to define) in a definition question is critical since if the target term is not correctly identified, then all subsequent modules have no chance of providing relevant nuggets. In this paper, we present a me...

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Detalles Bibliográficos
Autores principales: López López, Aurelio, Martínez, Carmen
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
Question-answering (fact retrieval) systems
question sentence
tag
QTag
Hidden Markov Model
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/23918
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-23918
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Question-answering (fact retrieval) systems
question sentence
tag
QTag
Hidden Markov Model
spellingShingle Ciencias Informáticas
Question-answering (fact retrieval) systems
question sentence
tag
QTag
Hidden Markov Model
López López, Aurelio
Martínez, Carmen
Analysing definition questions by two machine learning approaches
topic_facet Ciencias Informáticas
Question-answering (fact retrieval) systems
question sentence
tag
QTag
Hidden Markov Model
description In automatic question answering, the identification of the correct target term (i.e. the term to define) in a definition question is critical since if the target term is not correctly identified, then all subsequent modules have no chance of providing relevant nuggets. In this paper, we present a method to tag a question sentence experimenting with two learning approaches: QTag and Hidden Markov Model. We tested the methods in five collections of questions, PILOT, TREC 2003, TREC 2004, CLEF 2004 and CLEF 2005. We performed ten-fold cross validation for each collection and we also tested with all questions together. The best accuracy rates for each collection were obtained using QTag, but with all questions together the best accuracy rate is obtained using HMM.
format Objeto de conferencia
Objeto de conferencia
author López López, Aurelio
Martínez, Carmen
author_facet López López, Aurelio
Martínez, Carmen
author_sort López López, Aurelio
title Analysing definition questions by two machine learning approaches
title_short Analysing definition questions by two machine learning approaches
title_full Analysing definition questions by two machine learning approaches
title_fullStr Analysing definition questions by two machine learning approaches
title_full_unstemmed Analysing definition questions by two machine learning approaches
title_sort analysing definition questions by two machine learning approaches
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/23918
work_keys_str_mv AT lopezlopezaurelio analysingdefinitionquestionsbytwomachinelearningapproaches
AT martinezcarmen analysingdefinitionquestionsbytwomachinelearningapproaches
bdutipo_str Repositorios
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