Text representation: from vector to tensor

In this paper, we propose a text representation model, Tensor Space Model (TSM), which models the text by multilinear algebraic high-order tensor instead of the traditional vector. Supported by techniques of multilinear algebra, TSM offers a potent mathematical framework for analyzing the multifacto...

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Bibliographic Details
Main Authors: Ning Liu, Benyu Zhang, Jun Yan, Zheng Chen, Wenyin Liu, Fengshan Bai, Leefeng Chien
Format: Conference Proceeding
Language:English
Subjects:
Asia
Computer science
Data mining
Indexing
Information retrieval
Large scale integration
Matrix decomposition
Principal component analysis
Singular value decomposition
Tensile stress
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Summary:In this paper, we propose a text representation model, Tensor Space Model (TSM), which models the text by multilinear algebraic high-order tensor instead of the traditional vector. Supported by techniques of multilinear algebra, TSM offers a potent mathematical framework for analyzing the multifactor structures. TSM is further supported by certain introduced particular operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for dimension reduction and other applications. Experimental results on the 20 Newsgroups dataset show that TSM is constantly better than VSM for text classification.
ISSN:1550-4786
2374-8486
DOI:10.1109/ICDM.2005.144