Supervised WSD techniques

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.