Semi-Supervised Word Sense Disambiguation via Context Weighting

Abstract

Word sense disambiguation as a central research topic in natural language processing can promote the development of many applications such as information retrieval, speech synthesis, machine translation, summarization and question answering. Previous approaches can be grouped into three categories: supervised, unsupervised and knowledge-based. The accuracy of supervised methods is the highest, but they suffer from knowledge acquisition bottleneck. Unsupervised method can avoid knowledge acquisition bottleneck, but its effect is not satisfactory. With the built-up of large-scale knowledge, knowledge-based approach has attracted more and more attention. This paper introduces a new context weighting method, and based on which proposes a novel semi-supervised approach for word sense disambiguation. The significant contribution of our method is that thesaurus and machine learning techniques are integrated in word sense disambiguation. Compared with the state of the art on the test data of the English all words disambiguation task in Sensaval-3, our method yields obvious improvements over existing methods in nouns, adjectives and verbs disambiguation.

Metaheuristic for Word Sense Disambiguation: a Review

“…The Yarowsky (1995) algorithm and (Blum & Mitchell, 1998) were early examples of such algorithms. A semi-supervised model was recommended by Zhao, [72] that used context weighting technique founded on the paradigm of Phrase Structure Tree (PTree) and Dependency Relation Graph (DGraph). With respect to nouns, verbs, and adjectives disambiguation, these techniques result in considerable advances over CW-WSD techniques.…”

Chinese Word Sense Disambiguation Based on Beam Search

“…Klapaftis analyses evaluation results in Semeval 2010 word sense induction task, and gives a new evaluation point for sense induction [7]. Zhao integrates thesaurus and machine learning algorithms into WSD, and provides a new semi-supervised WSD method in which contexts are weighed [8].…”