Rau, L.F.; Jacobs, P.S.; Zernik, U.: Information extraction and text summarization using linguistic knowledge acquisition (1989)
0.03
0.030980267 = product of:
0.061960533 = sum of:
0.033637732 = weight(_text_:information in 6683) [ClassicSimilarity], result of:
0.033637732 = score(doc=6683,freq=12.0), product of:
0.08850355 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.050415643 = queryNorm
0.38007212 = fieldWeight in 6683, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0625 = fieldNorm(doc=6683)
0.028322803 = product of:
0.056645606 = sum of:
0.056645606 = weight(_text_:organization in 6683) [ClassicSimilarity], result of:
0.056645606 = score(doc=6683,freq=2.0), product of:
0.17974974 = queryWeight, product of:
3.5653565 = idf(docFreq=3399, maxDocs=44218)
0.050415643 = queryNorm
0.31513596 = fieldWeight in 6683, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5653565 = idf(docFreq=3399, maxDocs=44218)
0.0625 = fieldNorm(doc=6683)
0.5 = coord(1/2)
0.5 = coord(2/4)
- 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
- Source
- Information processing and management. 25(1989) no.4, S.419-428