Learning Expressive Models for Word Sense Disambiguation

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 41–48, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Learning Expressive Models for Word Sense Disambiguation Lucia Specia NILC/ICMC University of São Paulo Caixa Postal 668, 13560-970 São Carlos, SP, Brazil lspecia@icmc.usp.br Mark Stevenson Department of Computer Science University of Sheffield Regent Court, 211 Portobello St. Sheffield, S1 4DP, UK marks@dcs.shef.ac.uk Maria das Graças V. Nunes NILC/ICMC University of São Paulo Caixa Postal 668, 13560-970 São Carlos, SP, Brazil gracan@icmc.usp.br Abstract We present a novel approach to the word sense disambiguation problem which makes use of corpus-based evidence com- bined with background knowledge. Em- ploying an inductive logic programming algorithm, the approach generates expres- sive disambiguation rules which exploit several knowledge sources and can also model relations between them. The ap- proach is evaluated in two tasks: identifica- tion of the correct translation for a set of highly ambiguous verbs in English- Portuguese translation and disambiguation of verbs from the Senseval-3 lexical sam- ple task. The average accuracy obtained for the multilingual task outperforms the other machine learning techniques investigated. In the monolingual task, the approach per- forms as well as the state-of-the-art sys- tems which reported results for the same set of verbs. 1 Introduction Word Sense Disambiguation (WSD) is concerned with the identification of the meaning of ambi- guous words in context. For example, among the possible senses of the verb “run” are “to move fast by using one's feet” and “to direct or control”. WSD can be useful for many applications, includ- ing information retrieval, information extraction and machine translation. Sense ambiguity has been recognized as one of the most important obstacles to successful language understanding since the ear- ly 1960’s and many techniques have been pro- posed to solve the problem. Recent approaches focus on the use of various lexical resources and corpus-based techniques in order to avoid the sub- stantial effort required to codify linguistic know- ledge. These approaches have shown good results; particularly those using supervised learning (see Mihalcea et al., 2004 for an overview of state-of- the-art systems). However, current approaches rely on limited knowledge representation and modeling techniques: traditional machine learning algorithms and attribute-value vectors to represent disambigu- ation instances. This has made it difficult to exploit deep knowledge sources in the generation of the disambiguation models, that is, knowledge that goes beyond simple features extracted directly from the corpus, like bags-of-words and colloca- tions, or provided by shallow natural language tools like part-of-speech taggers. In this paper we present a novel approach for WSD that follows a hybrid strategy, i.e. combines knowledge and corpus-based evidence, and em- ploys a first-order formalism to allow the represen- tation of deep knowledge about disambiguation examples together with a powerful modeling tech- nique to induce theories based on the examples and background knowledge. This is achieved using Inductive Logic Programming (ILP) (Muggleton, 1991), which has not yet been applied to WSD. Our hypothesis is that by using a very expres- …

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