Name
Abstract | ||
|---|---|---|
Latent Semantic Indexing (LSI) is a novel approach to
information retrieval that attempts to model the
underlying structure of term associations by transforming
the traditional representation of documents as vectors of
weighted term frequencies to a new coordinate space where
both documents and terms are represented as linear
combinations of underlying semantic factors. In previous
research, LSI has produced a small improvement in
retrieval performance. In this paper, we apply LSI to the
routing task, which operates under the assumption that a
sample of relevant and non-relevant documents is available
to use in constructing the query. Once again, LSI slightly
improves performance. However, when LSI is used is
conduction with statistical classification, there is a
dramatic improvement in performance. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1007/978-1-4471-2099-5_29 | SIGIR |
Keywords | Field | DocType |
latent semantic indexing,information retrieval,term frequency | Human–computer information retrieval,Information retrieval,Computer science,Document clustering,Explicit semantic analysis,Artificial intelligence,Probabilistic latent semantic analysis,Natural language processing,Term Discrimination,Vector space model,Concept search,Visual Word | Conference |
ISBN | Citations | PageRank |
0-387-19889-X | 90 | 18.00 |
References | Authors | |
14 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David A. Hull | 1 | 1282 | 214.27 |