(Data|Text) Mining - Word-sense disambiguation (WSD)

Thomas Bayes


Word-sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings.


A rich variety of techniques have been researched:

  • dictionary-based methods that use the knowledge encoded in lexical resources,
  • supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples,
  • completely unsupervised methods that cluster occurrences of words, thereby inducing word senses.

Supervised learning approaches have been the most successful algorithms to date.

Documentation / Reference

Share this page:
Follow us:
Task Runner