Terminology extraction (also known asterm extraction,glossary extraction, termrecognition, or terminologymining) is a subtask ofinformation extraction. The goal of terminology extraction is to automatically extract relevant terms from a givencorpus.[1]
One of the first steps to model aknowledge domain is to collect a vocabulary of domain-relevant terms, constituting the linguistic surface manifestation of domainconcepts. Several methods to automatically extract technical terms from domain-specific document warehouses have been described in the literature.[5][6][7][8][9][10][11][12][13][14][15][16][17]
Typically, approaches to automatic term extraction make use of linguistic processors (part of speech tagging,phrase chunking) to extract terminological candidates, i.e. syntactically plausible terminologicalnoun phrases. Noun phrases include compounds (e.g. "credit card"), adjective noun phrases (e.g. "local tourist information office"), and prepositional noun phrases (e.g. "board of directors"). In English, the first two (compounds and adjective noun phrases) are the most frequent.[18] Terminological entries are then filtered from the candidate list using statistical andmachine learning methods. Once filtered, because of their low ambiguity and high specificity, these terms are particularly useful for conceptualizing a knowledge domain or for supporting the creation of adomain ontology or a terminology base. Furthermore, terminology extraction is a very useful starting point forsemantic similarity,knowledge management,human translation andmachine translation, etc.
The methods for terminology extraction can be applied toparallel corpora. Combined with e.g.co-occurrence statistics, candidates for term translations can be obtained.[19] Bilingual terminology can be extracted also from comparable corpora[20] (corpora containing texts within the same text type, domain but not translations of documents between each other).
^Alrehamy, Hassan H; Walker, Coral (2018). "SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation".Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing. Vol. 650. pp. 222–235.doi:10.1007/978-3-319-66939-7_19.ISBN978-3-319-66938-0.
^Collier, N.; Nobata, C.; Tsujii, J. (2002). "Automatic acquisition and classification of terminology using a tagged corpus in the molecular biology domain".Terminology.7 (2):239–257.doi:10.1075/term.7.2.07col.
^Sclano, F. andVelardi, P.Archived 2012-05-04 at theWayback Machine.TermExtractor: a Web Application to Learn the Shared Terminology of Emergent Web Communities. To appear in Proc. of the 3rd International Conference on Interoperability for Enterprise Software and Applications (I-ESA 2007). Funchal (Madeira Island), Portugal, March 28–30th, 2007.
^Alrehamy, Hassan H; Walker, Coral (2018). "SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation".Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing. Vol. 650. pp. 222–235.doi:10.1007/978-3-319-66939-7_19.ISBN978-3-319-66938-0.
