lextraction

Lextraction is a term used in linguistics, corpus linguistics, and natural language processing to describe the extraction of lexical information from natural language text. It encompasses identifying and harvesting words, lemmas, multiword expressions, and related lexical attributes such as part of speech, frequency, sentiment, and semantic associations. The goal is to produce structured resources or datasets that support further analysis and NLP tasks.

Typical methods begin with preprocessing and tokenization, followed by normalization (case folding, stemming, or lemmatization). Lexical items are then annotated with linguistic features using part-of-speech tagging, lemmatization, and named-entity recognition. Sophisticated workflows also detect multiword expressions, collocations, and term candidates for domain lexicons. The resulting outputs may be stored in lexicons, dictionaries, or annotated corpora and can be used for frequency analysis, semantic similarity, or training data for machine learning models.

Applications include building domain-specific glossaries and terminologies, creating lexical resources for low-resource languages, improving information retrieval and search, feeding machine translation and other NLP systems, and supporting linguistic research in lexicon usage, syntax, and discourse.

Challenges include language variation, morphologically rich languages, polysemy and sense disambiguation, domain shift, and noise in user-generated text. Evaluation is often done by comparing extracted items to gold standards or through downstream task performance.

See also information extraction, lexical resources, natural language processing, and text mining.