VUA-background : When to Use Background Information to Perform Word Sense Disambiguation

Maximizing Semantic Relatedness to Perform Word Sense Disambiguation

This article presents a method of word sense disambiguation that assigns a target word the sense that is most related to the senses of its neighboring words. We explore the use of measures of similarity and relatedness that are based on finding paths in a concept network, information content derived from a large corpus, and word sense glosses. We observe that measures of relatedness are useful ...

ITRI-97-04 Foreground and Background Lexicons and Word Sense Disambiguation for Information Extraction

A Hybrid Relational Approach for Word Sense Disambiguation

We propose a novel approach for word sense disambiguation which makes use of corpus-based evidence combined with background knowledge. Using an inductive logic programming technique, it generates expressive models which exploit several knowledge sources and also the relations between them. The approach is evaluated in two tasks: identification of the correct translation for verbs in English-Por...

Improving Japanese Zero Pronoun Resolution by Global Word Sense Disambiguation

This paper proposes unsupervised word sense disambiguation based on automatically constructed case frames and its incorporation into our zero pronoun resolution system. The word sense disambiguation is applied to verbs and nouns. We consider that case frames define verb senses and semantic features in a thesaurus define noun senses, respectively, and perform sense disambiguation by selecting th...

A Hybrid Model for Word Sense Disambiguation in English- Portuguese Machine Translation

We present the proposal for an approach to word sense disambiguation with application in machine translation from English to Brazilian Portuguese. This approach follows a hybrid natural language processing method, that is, a mixture of knowledge and corpus-based approaches. The main innovative feature is the formalism that we intend to use to represent the instances and the background knowledge...