Search (2 results, page 1 of 1)
- Did you mean:
- lcsh's%3a%22Internet research%22 2
- lcshs%3a%22Internet research%22 2
-
Chen, H.; Dhar, V.: Cognitive process as a basis for intelligent retrieval system design (1991)
0.01
0.009402524 = product of: 0.018805047 = sum of: 0.018805047 = product of: 0.037610095 = sum of: 0.037610095 = weight(_text_:research in 3845) [ClassicSimilarity], result of: 0.037610095 = score(doc=3845,freq=2.0), product of: 0.1491455 = queryWeight, product of: 2.8529835 = idf(docFreq=6931, maxDocs=44218) 0.05227703 = queryNorm 0.2521705 = fieldWeight in 3845, product of: 1.4142135 = tf(freq=2.0), with freq of: 2.0 = termFreq=2.0 2.8529835 = idf(docFreq=6931, maxDocs=44218) 0.0625 = fieldNorm(doc=3845) 0.5 = coord(1/2) 0.5 = coord(1/2)
- Abstract
- 2 studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of 5 computerised models of online document retrieval. These models were incorporated into a design of an 'intelligent' document-based retrieval system. Following a discussion of this system, discusses the broader implications of the research for the design of information retrieval sysems
-
Chen, H.; Martinez, J.; Kirchhoff, A.; Ng, T.D.; Schatz, B.R.: Alleviating search uncertainty through concept associations : automatic indexing, co-occurence analysis, and parallel computing (1998)
0.01
0.007051893 = product of: 0.014103786 = sum of: 0.014103786 = product of: 0.028207572 = sum of: 0.028207572 = weight(_text_:research in 5202) [ClassicSimilarity], result of: 0.028207572 = score(doc=5202,freq=2.0), product of: 0.1491455 = queryWeight, product of: 2.8529835 = idf(docFreq=6931, maxDocs=44218) 0.05227703 = queryNorm 0.18912788 = fieldWeight in 5202, product of: 1.4142135 = tf(freq=2.0), with freq of: 2.0 = termFreq=2.0 2.8529835 = idf(docFreq=6931, maxDocs=44218) 0.046875 = fieldNorm(doc=5202) 0.5 = coord(1/2) 0.5 = coord(1/2)
- Abstract
- In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurence analysis, we performed a large-scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400.000+ abstracts in an INSPEC computer engineering collection. Two system-generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compaed with the human-generated INSPEC subject thesaurus. Our user evaluation revealed that the system-generated thesauri were better than the INSPEC thesaurus in 'concept recall', but in 'concept precision' the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase 'variety' in search terms the thereby reduce search uncertainty