DocumentCode :
3464472
Title :
Significant term extraction by Higher Order SVD
Author :
Manna, Sukanya ; Petres, Zoltán ; Gedeon, Tom
Author_Institution :
Dept. of Comput. Sci., Australian Nat. Univ., Canberra, ACT
fYear :
2009
fDate :
30-31 Jan. 2009
Firstpage :
63
Lastpage :
68
Abstract :
In this paper, we present a novel method for term importance, called tensor term indexing (TTI). This extracts significant terms from a document as well as a coherent collection of document set. The basic idea of this approach is to represent the whole document collection in a term-sentence-document tensor and employs higher-order singular value decomposition (HOSVD) for important term extraction. TTI uses the lower rank approximation technique to reduce noise by eliminating anecdotal terms, to mitigate synonymy by merging the dimensions associated with terms that have similar meanings, and to mitigates polysemy, since components of polysemous words that point in the ldquorightrdquo direction are added to the components of words that share a similar meaning. Our evaluation shows that that TTI model can extract significant terms relevant to a topic from a small number of documents which term frequency and inverse document frequency (tfidf) cannot.
Keywords :
approximation theory; document handling; indexing; information retrieval; singular value decomposition; higher order singular value decomposition; inverse document frequency; rank approximation technique; significant term extraction; tensor term indexing; term frequency; term sentence document tensor; Automation; Computer science; Data mining; Databases; Frequency; Indexing; Information retrieval; Law; Singular value decomposition; Tensile stress;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applied Machine Intelligence and Informatics, 2009. SAMI 2009. 7th International Symposium on
Conference_Location :
Herl´any
Print_ISBN :
978-1-4244-3801-3
Electronic_ISBN :
978-1-4244-3802-0
Type :
conf
DOI :
10.1109/SAMI.2009.4956610
Filename :
4956610
Link To Document :
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