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
0.007908144 = product of:
0.03954072 = sum of:
0.03954072 = weight(_text_:system in 3458) [ClassicSimilarity], result of:
0.03954072 = score(doc=3458,freq=4.0), product of:
0.13391352 = queryWeight, product of:
3.1495528 = idf(docFreq=5152, maxDocs=44218)
0.04251826 = queryNorm
0.29527056 = fieldWeight in 3458, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.1495528 = idf(docFreq=5152, maxDocs=44218)
0.046875 = fieldNorm(doc=3458)
0.2 = coord(1/5)
- 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.