Combining classifiers in text categorization

Larkey, Leah S.; Croft, W. Bruce

doi:10.1145/243199.243276

Cited by 277 publications

(189 citation statements)

References 17 publications

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“…Unlike [3] we do not merge our classifiers by linear combination, because the RegEx module does not return a scoring consistent with the vector space system. Therefore the combination does not use the RegEx's score, and instead it uses the list returned by the vector space module as a reference list (RL), while the list returned by the regular expression module is used as boosting list (BL), which serves in order to improve the ranking of terms listed in RL.…”

Section: Resultsmentioning

confidence: 99%

“…Following [3] and as it is usual with retrieval systems, the core measure for the evaluation is based on the 11-point average precision. We provide the total number of relevant terms returned by the system on the complete run.…”

Section: Discussionmentioning

confidence: 99%

“…The problem is twofold: it concerns both the ability of the system to work with large concept sets, and its ability to learn and generalize regularities for rare events: Larkey and Croft [3] show how the frequency of the concept in the collection is a major parameter for learning-base text categorization tools. The remainder of this paper is organized as follows: the next section presents the collection and metrics used, as well as the basic classifiers.…”

Section: Mesh Mapping and Medlinementioning

confidence: 99%

See 2 more Smart Citations

Learning-Free Text Categorization

Ruch

Baud

Geissbühler

2003

Artificial Intelligence in Medicine

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Abstract. In this paper, we report on the fusion of simple retrieval strategies with thesaural resources in order to perform large-scale text categorization tasks. Unlike most related systems, which rely on training data in order to infer text-to-concept relationships, our approach can be applied with any controlled vocabulary and does not use any training data. The first classification module uses a traditional vector-space retrieval engine, which has been fine-tuned for the task, while the second classifier is based on regular variations of the concept list. For evaluation purposes, the system uses a sample of MedLine and the Medical Subject Headings (MeSH) terminology as collection of concepts. Preliminary results show that performances of the hybrid system are significantly improved as compared to each single system. For top returned concepts, the system reaches performances comparable to machine learning systems, while genericity and scalability issues are clearly in favor of the learningfree approach. We draw conclusion on the importance of hybrids strategies combining data-poor classifiers and knowledge-based terminological resources for general text mapping tasks.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Mesh Mapping and Medlinementioning

confidence: 99%

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Learning-Free Text Categorization

Ruch

Baud

Geissbühler

2003

Artificial Intelligence in Medicine

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“…A wide variety of learning approaches have been applied to TC, to name a few, Bayesian classification (Lewis and Ringuette 1994;Domingo and Pazzani 1996;Larkey and Croft 1996;Koller and Sahami 1997;Lewis 1998), decision trees (Weiss, Apte et al ;Fuhr and Buckley 1991;Cohen and Hirsh 1998;Li and Jain 1998), decision rule classifiers such as CHARADE (Moulinier and Ganascia 1996), or DL-ESC (Li and Yamanishi 1999), or RIPPER (Cohen and Hirsh 1998), or SCAR (Moulinier, Raskinis et al 1996), or SCAP-1 (Apté, Damerau et al 1994), multi-linear regression models (Yang and Chute 1994;Yang and Liu 1999), Rocchio method (Hull 1994;Ittner, Lewis et al 1995;Sable and Hatzivassiloglou 2000), Neural Networks (Schütze, Hull et al 1995;Wiener, Pedersen et al 1995;Dagan, Karov et al 1997;Ng, Goh et al 1997;Lam and Lee 1999;Ruiz and Srinivasan 1999), example based classifiers (Creecy 1991;Masand, Linoff et al 1992;Larkey 1999), support vector machines (Joachims 1998), Bayesian inference networks (Tzeras and Hartmann 1993;Wai and Fan 1997;Dumais, Platt et al 1998), genetic algorithms (Masand 1994;Clack, Farringdon et al 1997), and maximum entropy modelling (Manning and Schütze 1999).…”

Section: Machine Learning Approaches To Text Categorizationmentioning

confidence: 99%

“…Among these are the DIA association factor (Fuhr and Buckley 1991), chi-square (Yang and Pedersen 1997;Sebastiani, Sperduti et al 2000;Caropreso, Matwin et al 2001), NGL coefficient (Ng, Goh et al 1997;Ruiz and Srinivasan 1999), information gain Lewis and Ringuette 1994;Moulinier, Raskinis et al 1996;Yang and Pedersen 1997;Larkey 1998;Mladenic and Grobelnik 1998;Caropreso, Matwin et al 2001), mutual information (Larkey and Croft 1996;Wai and Fan 1997;Dumais, Platt et al 1998;Taira and Haruno 1999) odds ratio (Mladenic and Grobelnik 1998;Ruiz and Srinivasan 1999;Caropreso, Matwin et al 2001), relevancy score (Wiener, Pedersen et al 1995) and GSS coefficient (Galavotti, Sebastiani et al 2000). Three of the most popular methods are descrivbed briefly below.…”

Section: This Leads To the Term Frequency/inverse Document Frequency mentioning

confidence: 99%

Text and Hypertext Categorization

Benbrahim

Bramer

2009

Lecture Notes in Computer Science

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Automatic categorization of text documents has become an important area of research in the last two decades, with features that make it significantly more difficult than the traditional classification tasks studied in machine learning. A more recent development is the need to classify hypertext documents, most notably web pages. These have features that add further complexity to the categorization task but also offer the possibility of using information that is not available in standard text classification, such as metadata and the content of the web pages that point to and are pointed at by a web page of interest. This chapter surveys the state of the art in text categorization and hypertext categorization, focussing particularly on issues of representation that differentiate them from 'conventional' classification tasks and from each other.

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An experimental study in automatically categorizing medical documents

Ribeiro-Neto

Laender

Lima

2001

J. Am. Soc. Inf. Sci.

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In this article, we evaluate the retrieval performance of an algorithm that automatically categorizes medical documents. The categorization, which consists in assigning an International Code of Disease (ICD) to the medical document under examination, is based on well‐known information retrieval techniques. The algorithm, which we proposed, operates in a fully automatic mode and requires no supervision or training data. Using a database of 20,569 documents, we verify that the algorithm attains levels of average precision in the 70–80% range for category coding and in the 60–70% range for subcategory coding. We also carefully analyze the case of those documents whose categorization is not in accordance with the one provided by the human specialists. The vast majority of them represent cases that can only be fully categorized with the assistance of a human subject (because, for instance, they require specific knowledge of a given pathology). For a slim fraction of all documents (0.77% for category coding and 1.4% for subcategory coding), the algorithm makes assignments that are clearly incorrect. However, this fraction corresponds to only one‐fourth of the mistakes made by the human specialists.

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Combining classifiers in text categorization

Cited by 277 publications

References 17 publications

Learning-Free Text Categorization

Learning-Free Text Categorization

Text and Hypertext Categorization

An experimental study in automatically categorizing medical documents

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