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)
0.02
0.024327723 = product of:
0.048655447 = sum of:
0.048655447 = product of:
0.09731089 = sum of:
0.09731089 = weight(_text_:engineering in 5202) [ClassicSimilarity], result of:
0.09731089 = score(doc=5202,freq=2.0), product of:
0.27322882 = queryWeight, product of:
5.372528 = idf(docFreq=557, maxDocs=44218)
0.050856657 = queryNorm
0.35615164 = fieldWeight in 5202, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.372528 = idf(docFreq=557, 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