[jira] [Updated] (OPENNLP-757) Supervised WSD techniques

["Mondher Bouazizi (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/OPENNLP-757?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Mondher Bouazizi updated OPENNLP-757:
-------------------------------------
    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.

  was:
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.


> Supervised WSD techniques
> -------------------------
>
>                 Key: OPENNLP-757
>                 URL: https://issues.apache.org/jira/browse/OPENNLP-757
>             Project: OpenNLP
>          Issue Type: New Feature
>          Components: Machine Learning, POS Tagger, Sentence Detector, Stemmer
>            Reporter: Mondher Bouazizi
>              Labels: gsoc, gsoc2015, java, nlp, wsd
>
> 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.



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