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