TAILIEUCHUNG - Báo cáo khoa học: " Bilingual Bootstrapping for WSD"

Recent work on bilingual Word Sense Disambiguation (WSD) has shown that a resource deprived language (L1 ) can benefit from the annotation work done in a resource rich language (L2 ) via parameter projection. However, this method assumes the presence of sufficient annotated data in one resource rich language which may not always be possible. Instead, we focus on the situation where there are two resource deprived languages, both having a very small amount of seed annotated data and a large amount of untagged data. . | Together We Can Bilingual Bootstrapping for WSD Mitesh M. Khapra Salil Joshi Arindam Chatterjee Pushpak Bhattacharyya Department Of Computer Science and Engineering IIT Bombay Powai Mumbai 400076. miteshk salilj arindam pb @ Abstract Recent work on bilingual Word Sense Disambiguation WSD has shown that a resource deprived language L1 can benefit from the annotation work done in a resource rich language L2 via parameter projection. However this method assumes the presence of sufficient annotated data in one resource rich language which may not always be possible. Instead we focus on the situation where there are two resource deprived languages both having a very small amount of seed annotated data and a large amount of untagged data. We then use bilingual bootstrapping wherein a model trained using the seed annotated data of Li is used to annotate the untagged data of L2 and vice versa using parameter projection. The untagged instances of L1 and L2 which get annotated with high confidence are then added to the seed data of the respective languages and the above process is repeated. Our experiments show that such a bilingual bootstrapping algorithm when evaluated on two different domains with small seed sizes using Hindi L1 and Marathi L2 as the language pair performs better than monolingual bootstrapping and significantly reduces annotation cost. 1 Introduction The high cost of collecting sense annotated data for supervised approaches Ng and Lee 1996 Lee et al. 2004 has always remained a matter of concern for some of the resource deprived languages of the world. The problem is even more hard-hitting for multilingual regions . India which has more than 20 constitutionally recognized languages . To circumvent this problem unsupervised and knowledge 561 based approaches Lesk 1986 Walker and Amsler 1986 Agirre and Rigau 1996 McCarthy et al. 2004 Mihalcea 2005 have been proposed as an alternative but they have failed to deliver good accuracies. .

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