MetaCart Sign in to MyCiteSeerX

Include Citations | Advanced Search | Help

Disambiguated Search | Include Citations | Advanced Search | Help

BoosTexter: A Boosting-based System for Text Categorization (2000) [271 citations — 21 self]

Abstract:

This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting algorithms. We describe in detail an implementation, called BoosTexter, of the new boosting algorithms for text categorization tasks. We present results comparing the performance of BoosTexter and a number of other text-categorization algorithms on a variety of tasks. We conclude by describing the application of our system to automatic call-type identification from unconstrained spoken customer responses.

Citations

2331 Introduction to Modern Information Retrieval – Salton, McGill - 1983
1210 A decision-theoretic generalization of on-line learning and an application to boosting – Freund, Schapire - 1997
1048 Experiments with a new boosting algorithm – Freund, Schapire - 1996
654 Fast effective rule induction – Cohen - 1995
595 Relevance feedback in information retrieval – Rocchio - 1971
501 Boosting the margin: A new explanation for the effectiveness of voting methods – Schapire, Freund, et al. - 1998
400 Improved boosting algorithms using confidence-rated predictions – Schapire, Singer - 1999
299 Learning to filter netnews – Lang - 1995
222 Bagging, boosting, and C4.5 – Quinlan - 1996
213 A comparison of two learning algorithms for text categorization – LEWIS, RINGUETTE - 1994
197 Arcing classifiers – Breiman - 1998
194 Context sensitive learning methods for text categorization – Cohen - 1999
149 Heterogeneous uncertainty sampling for supervised learning.” ICML – Lewis, Catlett - 1994
147 Developments in Automatic Text Retrieval – Salton - 1991
135 Representation and learning in information retrieval – Lewis - 1992
127 Expert network: Effective and efficient learning from human decisions in text categorization and retrieval – Yang - 1994
105 Empirical support for winnow and weighted-majority based algorithms: results on a calendar scheduling domain – Blum - 1997
89 Feature selection, perceptron learning, and a usability case study for text categorization – Ng, Goh, et al. - 1997
79 C.: Boosting decision trees – Drucker, Cortes - 1995
75 Pruning adaptive boosting – Margineantu, Dietterich - 1997
74 Towards language independent automated learning of text categorization models – Apté, Damerau, et al. - 1994
72 An empirical evaluation of bagging and boosting – Maclin, Opitz - 1997
66 How may I help you – Gorin, Riccardi, et al. - 1997
66 Using output codes to boost multiclass learning problems – Schapire - 1997
59 Maximizing text-mining performance – Weiss, Apte, et al. - 1999
55 Using and combining predictors that specialize – Freund, Schapire, et al. - 1997
48 Text categorization of low quality images – Ittner, Lewis, et al. - 1995
41 Training text classifiers by uncertainty sampling – Lewis, Gale - 1994
37 Text categorization: a symbolic approach – Moulinier, Raˇskinis, et al. - 1996
23 Probabilistic information retrieval as a combination of abstraction, inductive learning, and probabilistic assumptions – Fuhr, Pfeifer - 1994
21 The automatic indexing system AIR/PHYS — from research to application – Biebricher, Fuhr, et al. - 1988
20 Towards automatic indexing: automatic assignment of controlledlanguage indexing and classification from free indexing – Field - 1975
20 Automatic acquisition of salient grammar fragments for call-type classification – Wright, Gorin, et al. - 1997
8 Hierarchically classifying docuemnts using very few words – Koller, Sahami - 1997
6 A probabilistic analysis of the Rochhio algorithm with TFIDF for text categorization – Joachims - 1997
6 Spoken language understanding for automated call routing – Riccardi, Gorin, et al. - 1997
View or Download | Add to My Collection | Correct Errors