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Word Sense Disambiguation using Aggregated Similarity based on WordNet Graph Representation

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  • Mădălina ZURINI

Abstract

The term of word sense disambiguation, WSD, is introduced in the context of text document processing. A knowledge based approach is conducted using WordNet lexical ontology, describing its structure and components used for the process of identification of context related senses of each polysemy words. The principal distance measures using the graph associated to WordNet are presented, analyzing their advantages and disadvantages. A general model for aggregation of distances and probabilities is proposed and implemented in an application in order to detect the context senses of each word. For the non-existing words from WordNet, a similarity measure is used based on probabilities of co-occurrences. The module of WSD is proposed for integration in the step of processing documents such as supervised and unsupervised classification in order to maximize the correctness of the classification. Future work is related to the implementation of different domain oriented ontologies.

Suggested Citation

  • Mădălina ZURINI, 2013. "Word Sense Disambiguation using Aggregated Similarity based on WordNet Graph Representation," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(3), pages 169-180.
    • Handle: RePEc:aes:infoec:v:17:y:2013:i:3:p:169-180
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