Ding, C.H.Q.: ¬A probabilistic model for Latent Semantic Indexing (2005)
0.00
0.001757696 = product of:
0.003515392 = sum of:
0.003515392 = product of:
0.007030784 = sum of:
0.007030784 = weight(_text_:a in 3459) [ClassicSimilarity], result of:
0.007030784 = score(doc=3459,freq=6.0), product of:
0.053105544 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046056706 = queryNorm
0.13239266 = fieldWeight in 3459, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=3459)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Latent Semantic Indexing (LSI), when applied to semantic space built an text collections, improves information retrieval, information filtering, and word sense disambiguation. A new dual probability model based an the similarity concepts is introduced to provide deeper understanding of LSI. Semantic associations can be quantitatively characterized by their statistical significance, the likelihood. Semantic dimensions containing redundant and noisy information can be separated out and should be ignored because their negative contribution to the overall statistical significance. LSI is the optimal solution of the model. The peak in the likelihood curve indicates the existence of an intrinsic semantic dimension. The importance of LSI dimensions follows the Zipf-distribution, indicating that LSI dimensions represent latent concepts. Document frequency of words follows the Zipf distribution, and the number of distinct words follows log-normal distribution. Experiments an five standard document collections confirm and illustrate the analysis.
- Type
- a
