Inducing shades of meaning by matrix methods: A First step towards thematic analysis of opinion

3rd International Conference on Advances in Semantic Processing - SEMAPRO 2009

Volumn , Issue , 2009, Pages 86-91

  Novakovich, David ^a   Bruza, Peter D ^a   Sitbon, Laurianne ^a,b  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b National ICT Centre   (Australia)

Author keywords

Non negative matrix factorization; Singular value decomposition; Word sense

Indexed keywords

CONDITIONAL ENTROPY; MATRIX METHODS; MATRIX REPRESENTATION; NON-NEGATIVE MATRIX; NON-NEGATIVE MATRIX FACTORIZATION; TAGGED DATA; THEMATIC ANALYSIS; WORD SENSE; WORD SENSE DISAMBIGUATION;

BLIND SOURCE SEPARATION; FACTORIZATION; LIGHT MEASUREMENT; SEMANTICS;

SINGULAR VALUE DECOMPOSITION;

EID: 74349125497     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SEMAPRO.2009.8     Document Type: Conference Paper

References (13)

1. B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.
2. H. Schutze and J. O. Pedersen, "A cooccurrencebased thesaurus and two applications to information retrieval," Inf. Process. Manage., vol. 33, no. 3, pp. 307-318, 1997.
3. P. Pantel and D. Lin, "Discovering word senses from text," in Proceedings of the eighth ACM SIGKDD international conference, New York, NY, USA, 2002, pp. 613-619.
4. R. Rapp, "Word sense discovery based on sense descriptor dissimilarity," in Proceedings of Ninth Machine Translation Summit, 2003, pp. 315-322.
5. D. B. Neill, "Fully automatic word sense induction by semantic clustering," Master's thesis, M.Phil. in Computer Speech, Text, and Internet Technology, Churchill College, 2002.
6. C. Burgess, "From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model," Behaviour Research Methods, Instruments & Computers, vol. 30, no. 2, pp. 188-198, 1998.
7. C. Burgess, , "Representing and resolving semantic ambiguity: A contribution from high-dimensional memory modeling," in On the Consequences of Meaning Selection: Perspectives on resolving lexical ambiguity. American Psychological Association, 2001, pp. 233-261.
8. C. Burgess, K. Livesay, and K. Lund, "Explorations in context space: words, sentences, discourse," Discourse Processes, vol. 25, no. 2&3, pp. 211-257, 1998.
9. C. Ding, "A Probabilistic Model for Latent Semantic Indexing," Journal of the American Society for Information Science and Technology, vol. 56, no. 6, pp. 597-608, 2005.
10. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization." Nature, vol. 401, no. 6755, pp. 788-791, October 1999.
11. E. Gaussier and C. Goutte, "Relation between plsa and nmf and implications," in Proc. 28th international ACM SIGIR conference on Research and development in information retrieval (SIGIR-05), 2005, pp. 601-602.
12. C. Ding, T. Li, and W. Peng, "On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing," Comput. Stat. Data Anal., vol. 52, no. 8, pp. 3913-3927, 2008.
13. T. Hofmann, "Probabilistic latent semantic indexing," in Proceedings of SIGIR'99. ACM Press, 1999.