TermExtraction

Term extraction, also known as keyword extraction or keyphrase extraction, is the task of automatically identifying terms or phrases that concisely characterize the content of a document or corpus. The output typically consists of noun phrases or domain-specific expressions and is used for indexing, search, summarization, topic modeling, and knowledge base construction. Term extraction can be applied to single documents or large collections and commonly supports monolingual and multilingual data.

Methods used in term extraction fall into statistical, linguistic, and hybrid categories. Statistical approaches rely on term frequency, document frequency, and co-occurrence patterns, with signals such as TF-IDF, mutual information, and likelihood ratios. Graph-based techniques (for example, TextRank and related algorithms) treat candidate terms as nodes in a graph and identify salience through ranking. Linguistic methods apply part-of-speech tagging and syntactic patterns to extract candidate noun phrases, often followed by a ranking step. Hybrid approaches combine statistical signals with linguistic cues and may incorporate supervised learning on annotated corpora.

Evaluation of term extraction typically uses gold-standard term lists and metrics such as precision, recall, and F1, sometimes measured at the top-k results. Challenges include polysemy, domain specificity, the extraction of multiword expressions, noisy data, and language variability. Term extraction differs from named-entity recognition in that it aims to identify domain terms and multiword expressions beyond conventional named entities, spanning various domains and languages.

Applications encompass document indexing and retrieval, search and recommendation, ontology population, terminology management, and assistive authoring tools.