collocationaware

Collocationaware is a term used in natural language processing to describe systems that explicitly account for collocations—frequent word co-occurrences and fixed multiword expressions—when representing text or generating language. A collocationaware model treats certain word sequences as units and uses information about which words tend to occur together, reducing the tendency to produce awkward or unidiomatic phrasing.

Implementation approaches include lexicon-based resources of collocations, statistical measures (such as pointwise mutual information) to flag likely collocations, and embeddings or models that operate at the phrase level. Some systems incorporate collocation features into traditional n-gram models or augment neural architectures with multiword units, while others detect MWEs on the fly and resegment input accordingly.

Benefits include improved fluency and naturalness in generated text, better handling of multiword expressions in translation and parsing, and more accurate disambiguation of words with multiple senses depending on context. Limitations include incomplete coverage, domain dependence, and added computational or data requirements.

Applications span machine translation, text generation, grammar checking, information retrieval, and language learning tools that emphasize phrase-level usage. An example is recognizing "take a break" as a common unit rather than treating "take" and "a break" separately, or handling idioms like "kick the bucket" as a fixed expression when appropriate.

Related concepts include collocation, multiword expressions, lexical collocations, and phrase-based or subword-aware language models.