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A Theoretical Analysis of Context-based Learning Algorithms for Word Sense Disambiguation

Paola Velardi, Alessandro Cucchiarelli

Word Sense Disambiguation (WSD) is a central task in the area of Natural Language Processing. In the past few years several context-based probabilistic and machine learning methods for WSD have been presented in literature. However, an important area of research that has not been given the attention it deserves is a formal analysis of the parameters affecting the performance of the learning task faced by these systems. Usually performance is estimated by measuring precision and recall of a specific algorithm for specific test sets and environmental conditions. Therefore, a comparison among different learning systems and an objective estimation of the difficulty of the learning task is extremely difficult. In this paper we propose, in the framework of Computational Learning theory, a formal analysis of the relations between accuracy of a context-based WSD system, the complexity of the context representation scheme, and the environmental conditions (e.g. the complexity of language domain and concept inventory).

Keywords: Natural Language Processing, Machine Learning

Citation: Paola Velardi, Alessandro Cucchiarelli: A Theoretical Analysis of Context-based Learning Algorithms for Word Sense Disambiguation. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.451-455.

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ECAI-2000 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Humboldt University on behalf of Gesellschaft für Informatik.