Word Sense Disambiguation (WSD) System

Jump to: navigation, search

A Word Sense Disambiguation (WSD) System is an NLP system that implements a WSD algorithm to solve a can solve a WSD task.



  • (Joshi et al., 2006) ⇒ Mahesh Joshi, Serguei Pakhomov, Ted Pedersen, Richard Maclin, and Christopher Chute. (2006). “An End-to-end Supervised Target-Word Sense Disambiguation System.” In: Proceedings of AAAI-2006 (Intelligent System Demonstration).
    • QUOTE: Word Sense Disambiguation (WSD) is the task of automatically deciding the sense of an ambiguous word based on its surrounding context. The correct sense is usually chosen from a predefined set of senses, known as the sense inventory. In target-word sense disambiguation the scope is limited to assigning meaning to occurrences of a few predefined target words in the given corpus of text. ... Most popular approaches to WSD use supervised machine learning methods to train a classifier using a set of labeled instances of the ambiguous word and create a statistical model. This model is then applied to unlabeled instances of the ambiguous word to decide their correct sense. In such approaches, the ability to run several experiments based on the choice of (i) features; and (ii) the classifier along with its parameters, is the key factor in determining the configuration that yields the best accuracy for the task under consideration. This is exactly what our system facilitates - an end-to-end interface for running several WSD experiments, with the choice of features using many existing and one new GATE (Cunningham et al. 2002) component and the choice of classifiers from WEKA (Witten & Frank 2005).