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 unsupervised techniques: these methods are based on unlabeled data, and do not exploit any manually tagged data.
The object of this project is to create a WSD solution (for English) that implements some unsupervised techniques. For example:
- Context Clustering
- Word Clustering
- Cooccurrence Graphs
- Overlap of Sense Definitions
- Selectional Preferences
- Structural Approaches