Lochbaum, K.E.; Streeter, A.R.: Comparing and combining the effectiveness of latent semantic indexing and the ordinary vector space model for information retrieval (1989)
0.01
0.013931636 = product of:
0.041794907 = sum of:
0.041794907 = product of:
0.083589815 = sum of:
0.083589815 = weight(_text_:indexing in 3458) [ClassicSimilarity], result of:
0.083589815 = score(doc=3458,freq=6.0), product of:
0.19018644 = queryWeight, product of:
3.8278677 = idf(docFreq=2614, maxDocs=44218)
0.049684696 = queryNorm
0.4395151 = fieldWeight in 3458, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.8278677 = idf(docFreq=2614, maxDocs=44218)
0.046875 = fieldNorm(doc=3458)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- A retrievalsystem was built to find individuals with appropriate expertise within a large research establishment on the basis of their authored documents. The expert-locating system uses a new method for automatic indexing and retrieval based on singular value decomposition, a matrix decomposition technique related to the factor analysis. Organizational groups, represented by the documents they write, and the terms contained in these documents, are fit simultaneously into a 100-dimensional "semantic" space. User queries are positioned in the semantic space, and the most similar groups are returned to the user. Here we compared the standard vector-space model with this new technique and found that combining the two methods improved performance over either alone. We also examined the effects of various experimental variables on the system`s retrieval accuracy. In particular, the effects of: term weighting functions in the semantic space construction and in query construction, suffix stripping, and using lexical units larger than a a single word were studied.
- Object
- Latent Semantic Indexing
