Name
Abstract | ||
|---|---|---|
Objectives: To cope with medical terms, which present a high variability of expression through a single natural language, in the sense that any term may be reformulated in hundred of different ways. Methods: A typology of term variants is presented as a systematic approach in order to favour the implementation of an exhaustive solution. Then, an algorithm able to handle all variants is designed. Results: Using MetaMap, single terms are analyzed with a success rate varying between 68 and 88%; the algorithm presented in this paper improves this situation. Conclusions: This experience shows that a semantic driven method, based on a thesaurus, provides a satisfactory solution to the problem of variability of a single term. The presented typology is representative of most variants in a language. |
Year | Venue | Keywords |
|---|---|---|
2004 | STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS | natural language processing,text retrieval,knowledge representation |
Field | DocType | Volume |
Categorization,Data mining,Computer science,Coping (psychology),Typology,Large numbers,Natural language,Natural language processing,Artificial intelligence,Syntax,First language,Text retrieval | Conference | 107 |
Issue | ISSN | Citations |
Pt 1 | 0926-9630 | 4 |
PageRank | References | Authors |
0.92 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert H Baud | 1 | 4 | 0.92 |
| Patrick Ruch | 2 | 119 | 16.27 |
| Arnaud Gaudinat | 3 | 62 | 12.47 |
| Paul Fabry | 4 | 4 | 3.97 |
| Christian Lovis | 5 | 349 | 55.53 |
| Antoine Geissbuhler | 6 | 815 | 49.75 |