Termidentifiering

Termidentifiering is the automatic process of identifying and extracting domain-specific terms from text. The goal is to produce a usable terminology or glossary that can support indexing, search, knowledge representation, and linguistic analysis. Terms may be single words or multiword expressions, such as neural network or clinical trial protocol.

The task combines linguistic analysis with statistical signals. Preprocessing includes tokenization, sentence splitting, and part-of-speech tagging; candidate terms are often generated from noun phrases or syntactic patterns. A scoring step ranks candidates by their likelihood of being terms, balancing domain relevance with discriminability.

Common approaches are rule-based methods that rely on morpho-syntactic patterns, statistical methods that use frequency-based measures (such as tf-idf, mutual information, or log-likelihood), and domain-specific measures like C-value and NC-value. More recently, supervised and semi-supervised learning uses annotated corpora to train classifiers that distinguish terms from non-terms, sometimes leveraging word embeddings and contextual features. Hybrid systems combine these signals.

Evaluation typically uses precision, recall, and F1 against a gold-standard term list or glossary. Performance depends on domain specifics, language, and annotation conventions. Tools and corpora for termidentifiering exist for many domains and languages, though coverage remains uneven.

Applications include building domain terminologies for ontologies and knowledge graphs, improving information retrieval and indexing, supporting automatic glossary generation, aiding clustering and document classification, and facilitating terminology governance in organizations.

Key challenges include identifying boundaries of multiword terms, handling polysemy and homonymy, domain drift, cross-domain variability, and language-specific phenomena such as compounding and inflection. Ambiguity between term-like expressions and general language requires careful disambiguation and validation.