idiomspotting

Idiomspotting is the task in natural language processing and linguistics of identifying idiomatic expressions within text, and distinguishing them from literal phrases. An idiom is a sequence whose figurative meaning is not predictable from its parts. Idiomspotting typically involves detecting multiword expressions that behave as single units in meaning, and determining the boundary between idiomatic and non-idiomatic usage in a given context.

Approaches to idiomspotting range from rule-based systems that rely on dictionaries of idioms and fixed variations, to data-driven methods that learn patterns from annotated corpora. Traditional methods use lexical cues, syntactic patterns, and collocation statistics. Modern approaches frequently use machine learning, including sequence labeling with conditional random fields, or neural models such as LSTMs and transformers, often augmented with context-aware features or dedicated idiom inventories.

Challenges include noncanonical or flexible surface forms, partial idioms, idioms with literal senses in some contexts, cross-lingual idioms, and domain-specific expressions. Idioms can vary in form, tense, inflection, or be catalyzed by metaphors. Annotated resources are scarce for many languages, making cross-domain adaptation difficult. Evaluation relies on precision, recall, and F1, sometimes requiring careful annotation of boundaries and sense.

Applications of idiomspotting include improving machine translation, sentiment analysis, question answering, and language learning tools. Accurate detection helps translate idioms into culturally appropriate equivalents and reduces misinterpretation of figurative language. The field intersects with research on multiword expressions, figurative language, and metaphor detection.