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  1. OpenNLP
  2. OPENNLP-757

Supervised WSD techniques

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Details

    • New Feature
    • Status: Open
    • Major
    • Resolution: Unresolved
    • None
    • None
    • wsd

    Description

      The objective of Word Sense Disambiguation (WSD) is to determine which sense of a word is meant in a particular context. Therefore, WSD is a classification task, where the classes are the different senses of the ambiguous word.

      Different techniques are proposed in the academic literature, which fall mainly into two categories: Supervised and Unsupervised.

      For this component, we focus on supervised techniques: these approaches use machine-learning techniques to learn a classifier from labeled training sets.

      The object of this project is to create a WSD solution (for English) that implements some supervised techniques. For example:

      • Decision Lists
      • Decision Trees
      • Naive Bayes
      • Neural Networks
      • Exemplar-Based or Instance-Based Learning
      • Support Vector Machines
      • Ensemble Methods
      • Semi-supervised Disambiguation
      • Etc.

      Attachments

        1. opennlp-wsd-supervised.patch
          6.44 MB
          Mondher Bouazizi
        2. sup.patch
          6.47 MB
          Mondher Bouazizi
        3. supervised.patch
          68 kB
          Mondher Bouazizi
        4. sup-clean.patch
          71 kB
          Mondher Bouazizi
        5. wsd-supervised-20150703.patch
          158 kB
          Mondher Bouazizi

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            mondher Mondher Bouazizi
            mondher Mondher Bouazizi

            Dates

              Created:
              Updated:

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