Class description generation for clustering and categorization

Details Class description generation for clustering and categorization Class description generation for clustering and categorization

2005-12-20

2010-10-12

Sked, Matthew J (Department: 2626)

Data processing: speech signal processing, linguistics, language

Linguistics

Natural language

C704S001000, C707S711000, C707S737000

Reexamination Certificate

active

07813919

ABSTRACT:
A class is to be characterized of a probabilistic classifier or clustering system that includes probabilistic model parameters. For each of a plurality of candidate words or word combinations, divergence of the class from other classes is computed based on one or more probabilistic model parameters profiling the candidate word or word combination. One or more words or word combinations are selected for characterizing the class as those candidate words or word combinations for which the class has substantial computed divergence from the other classes.

REFERENCES:
patent: 5703964 (1997-12-01), Menon et al.
patent: 5857179 (1999-01-01), Vaithyanathan et al.
patent: 6104835 (2000-08-01), Han
patent: 6137911 (2000-10-01), Zhilyaev
patent: 6424971 (2002-07-01), Kreulen et al.
patent: 6862586 (2005-03-01), Kreulen et al.
patent: 6931347 (2005-08-01), Boedi et al.
patent: 7031909 (2006-04-01), Mao et al.
patent: 7085771 (2006-08-01), Chung et al.
patent: 2003/0167163 (2003-09-01), Glover et al.
patent: 2003/0233232 (2003-12-01), Fosler-Lussier et al.
patent: 2005/0187892 (2005-08-01), Goutte et al.
patent: 2006/0093188 (2006-05-01), Blake et al.
patent: 2006/0136410 (2006-06-01), Gaussier et al.
patent: 2006/0287848 (2006-12-01), Li et al.
patent: 2007/0005340 (2007-01-01), Goutte et al.
patent: 2007/0005639 (2007-01-01), Gaussier et al.
patent: 2007/0067289 (2007-03-01), Novak
Dhillon, I. Mallela, S. Kumar, R. “A divisive information-theoretic feature clustering algorithm for text classification” Journal of Machine Learning Research 2003, 1265-1287.
Tomokiyo, T. Hurst, M. “A language model approach to keyphrase extraction” Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment, p. 33-40, Jul. 12, 2003.
Dobrokhotov, P. et al. “Combining NLP and probabilistic categorization for document and term selection for Swiss-Prot medical annotation” Bioinformatics, vol. 19, pp. i91-i94, 2003.
Dobrokhotov et al., “Combining NLP and probabilistic categorization for document and term selection for Swiss-Prot medical annotation,”Bioinformatics, vol. 19 Suppl. 1, pp. i91-i94, 2003.
Radev et al., “Automatic summarization of search engine hit lists,” ACL Workshop on Recent Advances in NLP and IR, pp. 99-109, 2000.
Dobrokhotov et al., “Combining NLP and Probabilistic Categorisation for . . . ,” Oxford University Press, pp. 1-3, (2001).
Gaussier et al, “A Hierarchical Model for Clustering and Categorising Documents,” Advances in Information Retrieval, pp. 229-247, (2002).
Goutte et al., “Corpus-Basedvs.Model-Based Selection of Relevant Features,” Proceedings of CORIA04, pp. 75-88, (2004).
Glover et al., “Using Web Structure for Classifying and Describing Web Pages,” published at WWW2002, 8 pp, (2002).
Glover et al., “Inferring Hierarchical Descriptions,” published at CIKM2002, 8 pp, (2002).
Popescul et al., “Automatic Labeling of Document Clusters,” 16 pp, at http://www.cis.upenn.edu/˜popescul/Publications/popescal00labeling.pdf, (2000).

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