Abstract
Question classification is one of the first tasks carried out in a Question Answering system. In this paper we present a multilingual question classification system based on machine learning techniques. We use Support Vector Machines to classify the questions. All the features needed to train and test this method are automatically extracted through statistical information in an unsupervised way, comparing Poisson distributions of single words in two plain corpora of questions and documents. Thus, we need nothing but plain text to train the system, obtaining a flexible approach easy to adapt to new languages and domains. We have tested it on a bilingual corpus of questions in English and Spanish.
This work has been developed in the framework of the project CICYT R2D2 (TIC2003-07158-C04).
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Tomás, D., Vicedo, J.L., Bisbal, E., Moreno, L. (2006). Automatic Feature Extraction for Question Classification Based on Dissimilarity of Probability Distributions. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds) Advances in Natural Language Processing. FinTAL 2006. Lecture Notes in Computer Science(), vol 4139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816508_15
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DOI: https://doi.org/10.1007/11816508_15
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