2,278 research outputs found
Inference and Evaluation of the Multinomial Mixture Model for Text Clustering
In this article, we investigate the use of a probabilistic model for
unsupervised clustering in text collections. Unsupervised clustering has become
a basic module for many intelligent text processing applications, such as
information retrieval, text classification or information extraction. The model
considered in this contribution consists of a mixture of multinomial
distributions over the word counts, each component corresponding to a different
theme. We present and contrast various estimation procedures, which apply both
in supervised and unsupervised contexts. In supervised learning, this work
suggests a criterion for evaluating the posterior odds of new documents which
is more statistically sound than the "naive Bayes" approach. In an unsupervised
context, we propose measures to set up a systematic evaluation framework and
start with examining the Expectation-Maximization (EM) algorithm as the basic
tool for inference. We discuss the importance of initialization and the
influence of other features such as the smoothing strategy or the size of the
vocabulary, thereby illustrating the difficulties incurred by the high
dimensionality of the parameter space. We also propose a heuristic algorithm
based on iterative EM with vocabulary reduction to solve this problem. Using
the fact that the latent variables can be analytically integrated out, we
finally show that Gibbs sampling algorithm is tractable and compares favorably
to the basic expectation maximization approach
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Automated feature selection is important for text categorization to reduce
the feature size and to speed up the learning process of classifiers. In this
paper, we present a novel and efficient feature selection framework based on
the Information Theory, which aims to rank the features with their
discriminative capacity for classification. We first revisit two information
measures: Kullback-Leibler divergence and Jeffreys divergence for binary
hypothesis testing, and analyze their asymptotic properties relating to type I
and type II errors of a Bayesian classifier. We then introduce a new divergence
measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure
multi-distribution divergence for multi-class classification. Based on the
JMH-divergence, we develop two efficient feature selection methods, termed
maximum discrimination () and methods, for text categorization.
The promising results of extensive experiments demonstrate the effectiveness of
the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data
Engineering. 14 pages, 5 figure
Persian Text Classification using naive Bayes algorithms and Support Vector Machine algorithm
One of the several benefits of text classification is to automatically assign document in predefined category is one of the primary steps toward knowledge extraction from the raw textual data. In such tasks, words are dealt with as a set of features. Due to high dimensionality and sparseness of feature vector results from traditional feature selection methods, most of the proposed text classification methods for this purpose lack performance and accuracy. Many algorithms have been implemented to the problem of Automatic Text Categorization that’s why, we tried to use new methods like Information Extraction, Natural Language Processing, and Machine Learning. This paper proposes an innovative approach to improve the classification performance of the Persian text. Naive Bayes classifiers which are widely used for text classification in machine learning are based on the conditional probability. we have compared the Gaussian, Multinomial and Bernoulli methods of naive Bayes algorithms with SVM algorithm. for statistical text representation, TF and TF-IDF and character-level 3 (3-Gram) [6,9] were used. Finally, experimental results on 10 newsgroups
Thumbs up? Sentiment Classification using Machine Learning Techniques
We consider the problem of classifying documents not by topic, but by overall
sentiment, e.g., determining whether a review is positive or negative. Using
movie reviews as data, we find that standard machine learning techniques
definitively outperform human-produced baselines. However, the three machine
learning methods we employed (Naive Bayes, maximum entropy classification, and
support vector machines) do not perform as well on sentiment classification as
on traditional topic-based categorization. We conclude by examining factors
that make the sentiment classification problem more challenging.Comment: To appear in EMNLP-200
Improving Text Classification with Semantic Information
The Air Force contracts a variety of positions, from Information Technology to maintenance services. There is currently no automated way to verify that quotes for services are reasonably priced. Small training data sets and word sense ambiguity are challenges that such a tool would encounter, and additional semantic information could help. This thesis hypothesizes that leveraging a semantic network could improve text-based classification. This thesis uses information from ConceptNet to augment a Naive Bayes Classifier. The leveraged semantic information would add relevant words from the category domain to the model that did not appear in the training data. The experiment compares variations of a Naive Bayes Classifier leveraging semantic information, including an Ensemble Model, against classifiers that do not. Results show a significant performance increase in a smaller data set but not a larger one. Out of all models tested, an Ensemble Based Classifier performs the best on both data sets. The results show that ConceptNet does not add enough new or relevant information to affect classifier performance on large data sets
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