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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Using Distributional Similarity to Organise BioMedical Terminology |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Journal Title | Terminology |
| Geographical Reach | international |
| Volume | 11 |
| Number | 1 |
| Page Range | 3 - 4 |
| Date | 2005 |
| Abstract Text | We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that havebeen accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are defined for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy, reaching an optimal value of 63.1%. |
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