Hybrid document indexing with spectral embedding

Authors:
Irina Matveeva;Gina-Anne Levow
Affiliations:
University of Chicago, Chicago, IL;University of Chicago, Chicago, IL
Venue:
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Year:
2007

Citing 3
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Abstract

Document representation has a large impact on the performance of document retrieval and clustering algorithms. We propose a hybrid document indexing scheme that combines the traditional bag-of-words representation with spectral embedding. This method accounts for the specifics of the document collection and also uses semantic similarity information based on a large scale statistical analysis. Clustering experiments showed improvements over the traditional tf-idf representation and over the spectral methods based solely on the document collection.