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1
Sparse Matrix Factorization: Applications to Latent Semantic Indexing
In: DTIC (2009)
Abstract: This article describes the use of Latent Semantic Indexing (LSI) and some of its variants for the TREC Legal batch task. Both folding-in and Essential Dimensions of LSI (EDLSI) appeared as if they might be successful for recall-focused retrieval on a collection of this size. Furthermore, we developed a new LSI technique, one which replaces the Singular Value Decomposition (SVD) with another technique for matrix factorization, the sparse column-row approximation (SCRA). We were able to conclude that all three LSI techniques have similar performance. Although our 2009 results showed significant improvement when compared to our 2008 results, the use of a better method for selection of the parameter K, which is the ranking that results in the best balance between precision and recall, appears to have provided the most benefit. ; Proceedings of the Eighteenth Text REtrieval Conference (TREC 2009) held in Gaithersburg, Maryland, November 17-20, 2009. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA). The original document contains color images.
Keyword: *INFORMATION RETRIEVAL; *LSI(LATENT SEMANTIC INDEXING); *SUBJECT INDEXING; BATCH PROCESSING; BOOLEAN ALGEBRA; CANADA; COLLECTION; DOCUMENTS; ESSENTIAL DIMENSIONS; Information Science; Linguistics; PARAMETERS; PRECISION; RECALL; SCRA(SPARSE COLUMN-ROW APPROXIMATION); SPARSE MATRIX; SVD(SINGULAR VALUE DECOMPOSITION); SYMPOSIA
URL: http://www.dtic.mil/docs/citations/ADA517770
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517770
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2
Survey of text mining : clustering, classification, and retrieval
Kogan, Jacob (Mitarb.); Cornelson, Michael (Mitarb.); Howland, Peg (Mitarb.). - New York [u.a.] : Springer, 2004
BLLDB
UB Frankfurt Linguistik
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