Supervised Word Sense Disambiguation

Supervised WSD seeks to learn classifiers that can assign senses to words in text using machine learning techniques. These classifiers are trained on manually sense tagged corpora. In general these classifiers are most suitable for lexical sample or target word disambiguation, where all the occurrences of a given word in a text is assigned a sense.

We have developed a number of different packages for supervised word sense disambiguation. Each of these has somewhat different capabilities.


Supervised Word Sense Disambiguation Development Team


The development of Supervised Word Sense Disambiguation methods has been supported by a National Science Foundation Faculty Early Career Development (CAREER) Program award (#0092784, 2001-2007). Logo NSF Logo

By: Ted Pedersen - tpederse AT d umn edu