Combining Naive Bayes and n-Gram Language Models for Text Classification

Combining Naive Bayes and n-Gram Language Models for Text Classification (2003)

Cached

Download Links

[www.people.umass.edu]
[ai.uwaterloo.ca]

Other Repositories/Bibliography

DBLP

by Fuchun Peng , Dale Schuurmans

Venue:	In 25th European Conference on Information Retrieval Research (ECIR
Citations:	26 - 2 self

BibTeX

@INPROCEEDINGS{Peng03combiningnaive,
    author = {Fuchun Peng and Dale Schuurmans},
    title = {Combining Naive Bayes and n-Gram Language Models for Text Classification},
    booktitle = {In 25th European Conference on Information Retrieval Research (ECIR},
    year = {2003},
    pages = {335--350},
    publisher = {Springer-Verlag}
}

Years of Citing Articles

Bookmark

OpenURL

Abstract

We augment the naive Bayes model with an n-gram language model to address two shortcomings of naive Bayes text classifiers.

Citations

3340	Pattern Classification and Scene Analysis - Duda, Hart - 1973
839	Machine learning in automated text categorization - Sebastiani - 2002
684	A language modeling approach to information retrieval - Ponte, Croft - 1998
632	Text classification from labeled and unlabeled documents using - Nigram, McCallum, et al.
631	J: An Empirical Study of Smoothing Techniques for Language Modeling - SF, Goodman - 1996
619	Nigam K: A comparison of event models for naïve Bayes text classification - McCallum - 1998
574	Text Compression - Bell, Cleary, et al. - 1990
510	Relevance weighting of search terms - Robertson, Jones - 1976
451	Bayesian Network Classifiers - Friedman, Geiger, et al. - 1997
413	An Evaluation of Statistical Approaches to Text Categorization - Yang - 1999
268	Naive (Bayes) at forty: The independence assumption in information retrieval - Lewis - 1998
253	Beyond independence: conditions for the optimality of the simple bayesian classifier - Domingos, Pazzani - 1996
228	Learning and Revising User Profiles: The Identification of Interesting Web Sites - Pazzani, Billsus - 1997
216	Using Language Models for Information Retrieval - Hiemstra - 2001
213	Context-sensitive learning methods for text categorization - Cohen, Singer - 1999
202	N-Gram-Based Text Categorization - Cavnar, Trenkle
149	On structuring probabilistic dependencies in stochastic language modeling. Computer Speech and Language - Ney, Essen, et al. - 1994
140	Pattern Classi and Scene Analysis - Duda, Hart - 1973
73	Feature Engineering for Text Classification - Scott, Matwin - 1999
65	Feature subset selection in text learning - Mladenic - 1998
58	Improving multiclass text classification with the support vector machine - Rennie, Rifkin - 2001
54	An empirical study of the naive bayes classifier - Rish - 2001
53	Automatic text categorization in terms of genre and author - Stamatatos, Fakotakis, et al. - 2000
49	Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches - Keogh, Pazzani - 1999
24	Bayesian network classi - Friedman, Geiger - 1997
23	On the naive bayes model for text categorization - Eyheramendy, Lewis, et al. - 2003
15	Statistical phrases for vector-space information retrieval - Turpin, Moffat - 1999
14	Using CompressionBased Language Models for Text Categorization - Teahan, Harper - 2001
10	A Comparative Study on Chinese Text Categorization Methods - He, Tan, et al. - 2000
10	On machine learning methods for chinese documents classification - He, Tan, et al. - 2003
6	Document language models, query models, and risk minimization for information retrieval - Laerty, Zhai - 2001
4	Learning Augmented Bayesian Classi A Comparison of Distribution-based and Classi Approaches - Keogh, Pazzanni - 1999
4	Feature Engineering for Text Classi - Scott, Matwin - 1999
3	Learnability of augmented naive Bayes in nominal domains - Zhang, Ling - 2001
2	Learning and Revising User Pro The identi cation of interesting web sites - Pazzani, Billsus - 1997
1	Employing Multiple Representations for Chinese - Kwok - 1999
1	Improving Multi-class Text Classi with Naive Bayes - Rennie - 2001
1	An Empirical Study of the Naive Bayes Classi - Rish - 2001