Mondher Bouazizi created OPENNLP-757:
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Summary: 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
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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