Rau, L.F.; Jacobs, P.S.; Zernik, U.: Information extraction and text summarization using linguistic knowledge acquisition (1989)
0.00
0.00395732 = product of:
0.01187196 = sum of:
0.01187196 = weight(_text_:a in 6683) [ClassicSimilarity], result of:
0.01187196 = score(doc=6683,freq=10.0), product of:
0.05209492 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.045180224 = queryNorm
0.22789092 = fieldWeight in 6683, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0625 = fieldNorm(doc=6683)
0.33333334 = coord(1/3)
- Abstract
- Storing and accessing texts in a conceptual format has a number of advantages over traditional document retrieval methods. A conceptual format facilitates natural language access to text information. It can support imprecise and inexact queries, conceptual information summarisation, and, ultimately, document translation. Describes 2 methods which have been implemented in a prototype intelligent information retrieval system calles SCISOR (System for Conceptual Information Summarisation, Organization and Retrieval). Describes the text processing, language acquisition, and summarisation components of SCISOR
- Type
- a
