Context-based methods for text categorisation

Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2004, Pages 578-579

  Hunnisett, D S ^a   Teahan, W J ^b  

^a CQRData   (United Kingdom)

^b BANGOR UNIVERSITY   (United Kingdom)

Author keywords

Language modeling; Text categorization

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; CHARACTER RECOGNITION; CLASSIFICATION (OF INFORMATION); CONTEXT SENSITIVE LANGUAGES; DATA REDUCTION; IMAGE COMPRESSION; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY;

CHARACTER CONTEXTS; CONTEXT-BASED METHODS; DATA STREAMS; TEXT CATEGORIZATION;

TEXT PROCESSING;

EID: 8644234402     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1008992.1009129     Document Type: Conference Paper

References (3)

1. S. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization. In Proceedings Int. Conf. on Inform. and Know. Management, pages 148-155, 1998.
2. D. V. Khmelev and W. J. Teahan. A repetition based measure for verification of text collections and for text categorization. In Proceedings of SIGIR 2003, pages 104-110, 2003.
3. W. J. Teahan and D. J. Harper. Using compression based language models for text categorization. In Lang. Modeling for Information Retrieval, pages 141-65. Kluwer Academic Publishers, 2003.