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Word Sense Disambiguation by Eneko Agirre download in pdf, ePub, iPad

These approaches are not verySupport vector machines and

These approaches are not very successful in practice, mainly because such a body of knowledge does not exist in a computer-readable format, outside very limited domains. Support vector machines and memory-based learning have been shown to be the most successful approaches, to date, probably because they can cope with the high-dimensionality of the feature space. For example, consider the two sentences. WordNet is a computational lexicon that encodes concepts as synonym sets e.

This library does the core functioning for our application. This will be used is storing the synonyms of the given data set and query tokens. This model has been extended to take into account systems that return a set of senses with weights for each occurrence.

For example, when disambiguating the words in pine cone, the definitions of the appropriate senses both include the words evergreen and tree at least in one dictionary. Different dictionaries and thesauruses will provide different divisions of words into senses.

Information retrieval Ambiguity has to be resolved in some queries. Handbook of Natural Language Processing, ed. There rises a slight confusion between lemmatization and stemming. Internal references Tomasz Downarowicz Entropy.