Word Sense Disambiguation

Words are ambiguous as the same word may have multiple meanings, Word Sense Disambiguation (WSD) is the process of deciding the sense of a word given its context.

WordNet is a large hand-annotated corpus giving word sense definitions and relations between word senses. A synset is a collection of word senses which all share the same meaning, and a gloss (description) of the general meaning of the set.

WordNet also describes the lexicographical categories of semantic fields, called supersenses. E.g. for nouns some of the supersenses are location, act, feeling...

WSD is a sequence labelling task, which involves picking the correct sense for each word based on the context.

A baseline algorithm would be to pick the most common word sense for each word.

A better method of WSD is to:

• contextual embeddings (e.g. BERT)
• For each sense of words in the corpus: average each of the token embeddings for the sense to get a sense embedding
• Then use a nearest neighbour classifier (cosine similarity) to find the nearest word sense

If we don't have sense for a word in our data, we can:

• Fall back to most common sense
• Use relations in Wordnet, in order:
  • If has children: average their embeddings
  • If has hypernyms: average their embeddings
  • else average the supersense embeddings