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About 7,005 papers for “Text categorization”

Text Categorization

Hirotoshi Taira · 2002

With the rapid spread of the Internet and the increase in on-line information, the technology for automatically classifying huge amounts of diverse text information has come to play a very important role. In the 1990s, the performance of computers improved sharply and it became possible to handle large quantities of text data. This led to the use of the machine learning approach, which is a method of creating classifiers automatically from the text data given in a category label. This approach provides excellent accuracy, reduces labor, and ensures conservative use of resources. This paper discusses the following three points related to text classification using machine learning. 1. How to perform highly precise classification by using a large number of word attributes (Chapter 3). 2. How to utilize the distribution of unlabeled examples for high precision when there are few labeled training examples (Chapter 4). 3. How to achieve a highly precise and efficient classification by assuming the existence of sub-categories and using active labeling (Chapter 5).

Cited by 0

Text Categorization

Megan Chenoweth, Min Song · IGI Global eBooks · 2011

Text categorization (TC) is a data mining technique for automatically classifying documents to one or more predefined categories. This paper will introduce the principles of TC, discuss common TC methods and steps, give an overview of the various types of TC systems, and discuss future trends. TC systems begin with a group of known categories and a set of training documents already assigned to a category, usually by a human expert. Depending on the system, the documents may undergo a process called dimensionality reduction, which reduces the number of words or features that the classifier evaluates during the learning process. The system then analyzes the documents and “learns” which words or features of each document caused it to be classified into a particular category. This is known as supervised learning, because it is based on human knowledge of the categories and their criteria. The learning process results in a classifier which can apply the rules it learned during the training phase to additional documents. Request access from your librarian to read this chapter's full text.

Cited by 0

Text Categorization Toolkit

Montejo-Ráez, Arturo · Americanae (AECID Library) · 2014

TECAT is a command-line tool for multi-label text categorization and evaluation. It is capable of combining multiple bases binary classifiers (built-in and external ones).

Cited by 0

Multilingual Text Categorization

Said Gadri · 2017

Text categorization is an important task in text mining process that consists in assigning a set of texts to a set of predefined categories based on learning algorithms. There exist two kinds of text categorization: monolingual and multilingual text categorization. The main problematic of this manuscript is how to exploit concepts and algorithms of machine learning in contextual categorization of multilingual texts. Our study on this subject allowed us to propose many solutions and provide many contributions, notably: (1) a simple, fast and effective algorithm to identify the language of a text in multilingual corpus. (2) An improved algorithm for Arabic stemming based on a statistical approach. Its main objective is to reduce the size of term vocabulary and thus increase the quality of the obtained categorization in TC and the effectiveness of search in IR. (3) A new multilingual stemmer which is general and completely independent of any language. (4) Application of new panoply of pseudo-distances to categorize texts of a big corpus such as Reuters21578 collection. All these solutions were the subject of many academic papers published in international conferences and journals.

Cited by 0

Arabic Text Categorization

Rehab Duwairi · 2005

Abstract: In this paper, we compare the performance of three classifiers for Arabic text categorization. In particular, the naïve Bayes, k-nearest-neighbors (knn), and distance-based classifiers were used. Unclassified documents were preprocessed by removing punctuation marks and stopwords. Each document is then represented as a vector of words (or of words and their frequencies as in the case of the naïve Bayes classifier). Stemming was used to reduce the dimensionality of feature vectors of documents. The accuracy of the classifiers is compared using recall, precision, error rate and fallout. The results of the experimentations that were carried out on an in-house collected Arabic text show that the naïve Bayes classifier outperforms the other two.

Cited by 0

Noisy text categorization

Alessandro Vinciarelli · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2005

This work presents categorization experiments performed over noisy texts. By noisy, we mean any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g., transcriptions of speech recordings extracted with a recognition system). The performance of a categorization system over the clean and noisy (Word Error Rate between approximately 10 and approximately 50 percent) versions of the same documents is compared. The noisy texts are obtained through handwriting recognition and simulation of optical character recognition. The results show that the performance loss is acceptable for Recall values up to 60-70 percent depending on the noise sources. New measures of the extraction process performance, allowing a better explanation of the categorization results, are proposed.

Cited by 53Open access

Text mining categorization

Lea Silvia Martins Gonçalves · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2002

Os avanços tecnológicos e científicos ocorridos nas últimas décadas têm proporcionado o desenvolvimento de métodos cada vez mais eficientes para o armazenamento e processamento de dados. Através da análise e interpretação dos dados, é possível obter o conhecimento. Devido o conhecimento poder auxiliar a tomada de decisão, ele se tornou um elemento de fundamental importância para diversas organizações. Uma grande parte dos dados disponíveis hoje se encontra na forma textual, exemplo disso é o crescimento vertiginoso no que se refere à internet. Como os textos são dados não estruturados, é necessário realizar uma série de passos para transformá-los em dados estruturados para uma possível análise. O processo denominado de Text Mining é uma tecnologia emergente e visa analisar grandes coleções de documentos. Esta dissertação de mestrado aborda a utilização de diferentes técnicas e ferramentas para Text Mining. Em conjunto com o módulo de Pré-processamento de textos, projetado e implementado por Imamura (2001), essas técnicas e ferramentas podem ser utilizadas para textos em português. São explorados alguns algoritmos utilizados para extração de conhecimento de dados, \"como: Vizinho mais Próximo, Naive Bayes, Árvore de Decisão, Regras de Decisão, Tabelas de Decisão e Support Vector Machines. Para verificar o comportamento desses algoritmos para textos em português, foram realizados alguns experimentos.

Cited by 0

Efficient Text Categorization

Marko Grobelnik, Dunja Mladenic · 1998

We present an approach to text categorization using machine learning techniques. The approach is developed and tested on large text hierarchy named Yahoo that is available on the Web. We handle the large number of features and training examples by taking into account hierarchical structure of examples and using feature subset selection for large text data. The large number of categories is handled separately for each testing example by pruning unpromising categories. In this way, the number of categories to be considered is cut to less than a half without degrading the system performance. Our experiments are performed using naive Bayesian classifier on text data using feature-vector document representation that includes n-grams instead of just single words (unigrams). Experimental evaluation on three domains constructed from Yahoo hierarchy shows that among several hundred categories the correct category is assigned probability over 0.99 when rather small number of features used. 1 Int...

Cited by 0

� Text Categorization/Classification

Sameer Maskey, Bishop Book, M Book · 2012

End of class, please fill in the survey Signup sheet for project partnerProject You should start thinking about the kind of project you want to do This is a semester long project Goal is for you to understand one NLP-ML problem of your interest in-depth Depending on your expertise and passion you may try to get a research paper out of it

Cited by 0

Fragments and Text Categorization

Eva Mráková · 2004

We introduce two novel methods of text categorization in which documents are split into fragments. We conducted experiments on English, French and Czech. In all cases, the problems referred to a binary document classification. We find that both methods increase the accuracy of text categorization. For the Naïve Bayes classifier this increase is significant. 1

Cited by 0