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Bilingual Text Classification using the IBM 1 Translation Model
BibTeX
@MISC{Civera_bilingualtext,
author = {Jorge Civera and Alfons Juan-císcar},
title = {Bilingual Text Classification using the IBM 1 Translation Model},
year = {}
}
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Abstract
Manual categorisation of documents is a time-consuming task that has been significantly alleviated with the deployment of automatic and machine-aided text categorisation systems. However, the proliferation of multilingual documentation has become a common phenomenon in many international organisations, while most of the current systems has focused on the categorisation of monolingual text. It has been recently shown that the inherent redundancy in bilingual documents can be effectively exploited by relatively simple, bilingual naive Bayes (multinomial) models. In this work, we present a refined version of these models in which this redundancy is explicitly captured by a combination of a unigram (multinomial) model and the well-known IBM 1 translation model. The proposed model is evaluated on two bilingual classification tasks and compared to previous work. 1.
Keyphrases
translation model bilingual text classification well-known ibm multilingual documentation bilingual document previous work many international organisation time-consuming task machine-aided text categorisation system inherent redundancy monolingual text common phenomenon bilingual naive bayes refined version current system bilingual classification task manual categorisation
