research papers for the project

Tuesday, April 21, 2009

http://trec.nist.gov/pubs/trec1/papers/11.txt

A comparison of document clustering techniques (2000)by Michael Steinbach, George Karypis, Vipin Kumar In KDD Workshop on Text Mining http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.125.9225

Projections for efficient document clustering (1997) by Hinrich Schutze, Craig Silverstein http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.8901

Latent semantic indexing (lsi): Trec-3 report (1995) [40 citations — 0 self] Download: by Susan T. Dumais http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.2241

Maximum likelihood from incomplete data via the EM algorithm (1977) by A. P. Dempster, N. M. Laird, D. B. Rdin Journal of the Royal Statistical Society, Series B http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.133.4884

Indexing by latent semantic analysis (1990) [1633 citations — 22 self] Download: by Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. L, Richard Harshman http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.8490

An algorithm to cluster documents based on relevance (2005) [2 citations — 0 self] Download: by Monica Desai, Amanda Spink Information Processing and Management http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.84.3463

A modified fuzzy art for soft document clustering (2002) [8 citations — 1 self] Download: by Ravikumar Kondadadi, Robert Kozma http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.90.6003

A new method of N-gram statistics for large number of n and automatic extraction of words and phrases from large text data of Japanese (1994)
by Makoto Nagao, Shinsuke Mori
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.8416

Class-Based n-Gram Models of Natural Language (1992)
by Peter F. Brown, Peter V. Desouza, Robert L. Mercer, Jenifer C. LaiComputational Linguistics
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.9919

Beyond word n-grams (1995)
by Fernando C. Pereira, Yoram Singer, Naftali TishbyIn Proceedings of the Third Workshop on Very Large Corpora
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.7

N-GRAM AND LOCAL CONTEXT ANALYSIS FOR PERSIAN TEXT RETRIEVAL
by Farhad Oroumchian A, Abolfazl Aleahmad A, Parsia Hakimian A, Farzad Mahdikhani A http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.8268

Modeling documents for structure recognition using generalized n-grams (1997) by R. Brugger, A. Zramdini, R. Ingoldin
Proceedings of ICDAR
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.2338

Discovering characteristic expressions from literary works: A new text analysis method beyond n-gram and kwic (2001)
by Masayuki Takeda, Tetsuya Matsumoto, Tomoko Fukuda, IchirÅ Nanri
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.5337

Information Retrieval

Understanding Inverse Document Frequency:On theoretical arguments for IDF

Stephen RobertsonMicrosoft Research7 JJ Thomson AvenueCambridge CB3 0FBUK(and City University, London, UK)

Using TF-IDF to Determine Word Relevance in Document Queries

Juan Ramos Department of Computer Science, Rutgers University, 23515 BPO Way, Piscataway, NJ, 08855

TEXT MINING

Ian H. WittenComputer Science, University of Waikato, Hamilton, New Zealand