Together We Can: Bilingual Bootstrapping for WSD

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 561–569, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics 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}@cse.iitb.ac.in Abstract Recent work on bilingual Word Sense Disam- biguation (WSD) has shown that a resource deprived language (L1) can benefit from the annotation work done in a resource rich lan- guage (L2) via parameter projection. How- ever, this method assumes the presence of suf- ficient annotated data in one resource rich lan- guage which may not always be possible. In- stead, 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 L1 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 lan- guages and the above process is repeated. Our experiments show that such a bilingual boot- strapping 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 boot- strapping 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 (e.g., India which has more than 20 constitutionally recognized languages). To cir- cumvent this problem, unsupervised and knowledge based approaches (Lesk, 1986; Walker and Amsler, 1986; Agirre and Rigau, 1996; McCarthy et al., 2004; Mihalcea, 2005) have been proposed as an al- ternative but they have failed to deliver good accura- cies. Semi-supervised approaches (Yarowsky, 1995) which use a small amount of annotated data and a large amount of untagged data have shown promise albeit for a limited set of target words. The above situation highlights the need for high accuracy re- source conscious approaches to all-words multilin- gual WSD. Recent work by Khapra et al. (2010) in this di- rection has shown that it is possible to perform cost effective WSD in a target language (L2) without compromising much on accuracy by leveraging on the annotation work done in another language (L1). This is achieved with the help of a novel synset- aligned multilingual dictionary which facilitates the projection of parameters learned from the Wordnet and annotated corpus of L1 to L2. This approach thus obviates the need for collecting large amounts of annotated corpora in multiple languages by rely- ing on sufficient annotated corpus in one resource rich language. However, in many situations such a pivot …

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