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11,457
Mapping an automated survey coding task into a probabilistic text categorization framework
- Proceedings of PorTAL 2002, 3rd International Conference on Advances in Natural Language Processing
, 2002
"... Abstract. This paper describes how to apply a probabilistic Text Categorization method to a different and new domain where documents are answers to open end questionnaires and codes viewed as categories consist of a hierarchical model. A reduced size training set may be used taking advantage of the ..."
Abstract
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Cited by 2 (2 self)
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Abstract. This paper describes how to apply a probabilistic Text Categorization method to a different and new domain where documents are answers to open end questionnaires and codes viewed as categories consist of a hierarchical model. A reduced size training set may be used taking advantage
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
, 1997
"... The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used in the ..."
Abstract
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Cited by 456 (1 self)
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The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used
Machine Learning in Automated Text Categorization
- ACM COMPUTING SURVEYS
, 2002
"... The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this p ..."
Abstract
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Cited by 1734 (22 self)
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The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach
An evaluation of statistical approaches to text categorization
- Journal of Information Retrieval
, 1999
"... Abstract. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine th ..."
Abstract
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Cited by 663 (22 self)
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Abstract. This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine
Inductive learning algorithms and representations for text categorization,”
- in Proceedings of the International Conference on Information and Knowledge Management,
, 1998
"... ABSTRACT Text categorization -the assignment of natural language texts to one or more predefined categories based on their content -is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text ..."
Abstract
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Cited by 652 (8 self)
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ABSTRACT Text categorization -the assignment of natural language texts to one or more predefined categories based on their content -is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text
A Re-Examination of Text Categorization Methods
, 1999
"... This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We f ..."
Abstract
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Cited by 853 (24 self)
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This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We
A Comparative Study on Feature Selection in Text Categorization
, 1997
"... This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI), ..."
Abstract
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Cited by 1320 (15 self)
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This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI
N-grambased text categorization
- In Proc. of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval
, 1994
"... Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors in ..."
Abstract
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Cited by 445 (0 self)
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Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors
BoosTexter: A Boosting-based System for Text Categorization
"... 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 catego ..."
Abstract
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Cited by 667 (20 self)
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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
RCV1: A new benchmark collection for text categorization research
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2004
"... Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which the data ..."
Abstract
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Cited by 663 (11 self)
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Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which
Results 1 - 10
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11,457
