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Knowledge and Information Systems

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01.08.2013 | Regular Paper

Minimally supervised question classification on fine-grained taxonomies

verfasst von: David Tomás, José L. Vicedo

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2013

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Abstract

This article presents a minimally supervised approach to question classification on fine-grained taxonomies. We have defined an algorithm that automatically obtains lists of weighted terms for each class in the taxonomy, thus identifying which terms are highly related to the classes and are highly discriminative between them. These lists have then been applied to the task of question classification. Our approach is based on the divergence of probability distributions of terms in plain text retrieved from the Web. A corpus of questions with which to train the classifier is not therefore necessary. As the system is based purely on statistical information, it does not require additional linguistic resources or tools. The experiments were performed on English questions and their Spanish translations. The results reveal that our system surpasses current supervised approaches in this task, obtaining a significant improvement in the experiments carried out.

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NII-NACSIS Test Collection for IR Systems: http://research.nii.ac.jp/ntcir/.

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In our experiments, we extended the concept of term to unigrams, bigrams, and trigrams.

We employed Yahoo! Search BOSS: http://developer.yahoo.com/search/boss/.

Binary logarithms were used in our experiments.

The value \(-1\) is not included in the interval because it always produces a negative \(\omega \).

Freely available at http://trec.nist.gov/data/qa.html.

All the sets of questions and seeds employed in the evaluation are available at http://www.dlsi.ua.es/~dtomas/resources/.

We employed the Apache Lucene search engine: http://lucene.apache.org.

The set of folds was the same as that used in the experiments with SVM.

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Titel: Minimally supervised question classification on fine-grained taxonomies
verfasst von: David Tomás
José L. Vicedo
Publikationsdatum: 01.08.2013
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 2/2013
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-012-0557-y

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