Details
Major 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.