PubMed · 11624376
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W E Gerabek. 1999. [Not Available].. https://pubmed.ncbi.nlm.nih.gov/11624376/
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This glossary aims to provide readers with some of the key terms that are relevant to a consideration of the relevance of social capital for health, and to introduce some of the debates on the concepts.
OBJECTIVE: We investigate the use of adjectival modification as a way of assessing the systematic use of linguistic phenomena to represent similar lexical or semantic features in the constituent terms of a vocabulary. METHODS: Terms consisting of one or more adjectival modifiers followed by a head noun are selected from disease and procedure terms in SNOMED. Frequently co-occurring adjectival modifiers are systematically combined with the contexts (i.e., terms minus modifier) of each modifier. The existence of these combinations is checked in both SNOMED and the entire UMLS Metathesaurus; the term corresponding to the context alone is similarly checked. Relationships among terms sharing a context and between each of these terms and their context are studied. RESULTS: Four pairs of modifiers were studied: (acute, chronic), (unilateral, bilateral), (primary, secondary), and (acquired, congenital). The numbers of contexts studied for each pair ranged from 73 to 974. The percentage of contexts associated with both modifiers ranged from 5 to 50% in SNOMED and from 10 to 60% in UMLS. The presence of the context term varied from 31 to 64% in SNOMED and from 43 to 79% in UMLS. Finally, 172 occurrences (9%) of synonymy between a modified term and the context term were found in SNOMED. One hundred and forty-five such occurrences (8%) were found in the entire Metathesaurus.
A prerequisite for all higher level information extraction tasks is the identification of unknown names in text. Today, when large corpora can consist of billions of words, it is of utmost importance to develop accurate techniques for the automatic detection, extraction and categorization of named entities in these corpora. Although named entity recognition might be regarded a solved problem in some domains, it still poses a significant challenge in others. In this work we focus on one of the more difficult tasks, the identification of protein names in text. This task presents several interesting difficulties because of the named entities variant structural characteristics, their sometimes unclear status as names, the lack of common standards and fixed nomenclatures, and the specifics of the texts in the molecular biology domain in which they appear. We describe how we approached these and other difficulties in the implementation of Yapex, a system for the automatic identification of protein names in text. We also evaluate Yapex under four different notions of correctness and compare its performance to that of another publicly available system for protein name recognition.