Domain Kernels for Word Sense Disambiguation

Proceedings of the 43rd Annual Meeting of the ACL, pages 403–410, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Domain Kernels for Word Sense Disambiguation Alfio Gliozzo and Claudio Giuliano and Carlo Strapparava ITC-irst, Istituto per la Ricerca Scientifica e Tecnologica I-38050, Trento, ITALY {gliozzo,giuliano,strappa}@itc.it Abstract In this paper we present a supervised Word Sense Disambiguation methodol- ogy, that exploits kernel methods to model sense distinctions. In particular a combi- nation of kernel functions is adopted to estimate independently both syntagmatic and domain similarity. We defined a ker- nel function, namely the Domain Kernel, that allowed us to plug “external knowl- edge” into the supervised learning pro- cess. External knowledge is acquired from unlabeled data in a totally unsupervised way, and it is represented by means of Do- main Models. We evaluated our method- ology on several lexical sample tasks in different languages, outperforming sig- nificantly the state-of-the-art for each of them, while reducing the amount of la- beled training data required for learning. 1 Introduction The main limitation of many supervised approaches for Natural Language Processing (NLP) is the lack of available annotated training data. This problem is known as the Knowledge Acquisition Bottleneck. To reach high accuracy, state-of-the-art systems for Word Sense Disambiguation (WSD) are de- signed according to a supervised learning frame- work, in which the disambiguation of each word in the lexicon is performed by constructing a dif- ferent classifier. A large set of sense tagged exam- ples is then required to train each classifier. This methodology is called word expert approach (Small, 1980; Yarowsky and Florian, 2002). However this is clearly unfeasible for all-words WSD tasks, in which all the words of an open text should be dis- ambiguated. On the other hand, the word expert approach works very well for lexical sample WSD tasks (i.e. tasks in which it is required to disambiguate only those words for which enough training data is pro- vided). As the original rationale of the lexical sam- ple tasks was to define a clear experimental settings to enhance the comprehension of WSD, they should be considered as preceding exercises to all-words tasks. However this is not the actual case. Algo- rithms designed for lexical sample WSD are often based on pure supervision and hence “data hungry”. We think that lexical sample WSD should regain its original explorative role and possibly use a min- imal amount of training data, exploiting instead ex- ternal knowledge acquired in an unsupervised way to reach the actual state-of-the-art performance. By the way, minimal supervision is the basis of state-of-the-art systems for all-words tasks (e.g. (Mihalcea and Faruque, 2004; Decadt et al., 2004)), that are trained on small sense tagged corpora (e.g. SemCor), in which few examples for a subset of the ambiguous words in the lexicon can be found. Thus improving the performance of WSD systems with few learning examples is a fundamental step towards the direction of designing a WSD system that works well on real texts. In addition, it is a …

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