Wright, L.W.; Nardini, H.K.G.; Aronson, A.R.; Rindflesch, T.C.: Hierarchical concept indexing of full-text documents in the Unified Medical Language System Information sources Map (1999)
0.03
0.030551035 = product of:
0.076377586 = sum of:
0.04841807 = weight(_text_:context in 2111) [ClassicSimilarity], result of:
0.04841807 = score(doc=2111,freq=2.0), product of:
0.17622331 = queryWeight, product of:
4.14465 = idf(docFreq=1904, maxDocs=44218)
0.04251826 = queryNorm
0.27475408 = fieldWeight in 2111, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
4.14465 = idf(docFreq=1904, maxDocs=44218)
0.046875 = fieldNorm(doc=2111)
0.027959513 = weight(_text_:system in 2111) [ClassicSimilarity], result of:
0.027959513 = score(doc=2111,freq=2.0), product of:
0.13391352 = queryWeight, product of:
3.1495528 = idf(docFreq=5152, maxDocs=44218)
0.04251826 = queryNorm
0.20878783 = fieldWeight in 2111, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.1495528 = idf(docFreq=5152, maxDocs=44218)
0.046875 = fieldNorm(doc=2111)
0.4 = coord(2/5)
- Abstract
- Full-text documents are a vital and rapidly growing part of online biomedical information. A single large document can contain as much information as a small database, but normally lacks the tight structure and consistent indexing of a database. Retrieval systems will often miss highly relevant parts of a document if the document as a whole appears irrelevant. Access to full-text information is further complicated by the need to search separately many disparate information resources. This research explores how these problems can be addressed by the combined use of 2 techniques: 1) natural language processing for automatic concept-based indexing of full text, and 2) methods for exploiting the structure and hierarchy of full-text documents. We describe methods for applying these techniques to a large collection of full-text documents drawn from the Health Services / Technology Assessment Text (HSTAT) database at the NLM and examine how this hierarchical concept indexing can assist both document- and source-level retrieval in the context of NLM's Information Source Map project
