3,444 research outputs found
A Route Confidence Evaluation Method for Reliable Hierarchical Text Categorization
Hierarchical Text Categorization (HTC) is becoming increasingly important
with the rapidly growing amount of text data available in the World Wide Web.
Among the different strategies proposed to cope with HTC, the Local Classifier
per Node (LCN) approach attains good performance by mirroring the underlying
class hierarchy while enforcing a top-down strategy in the testing step.
However, the problem of embedding hierarchical information (parent-child
relationship) to improve the performance of HTC systems still remains open. A
confidence evaluation method for a selected route in the hierarchy is proposed
to evaluate the reliability of the final candidate labels in an HTC system. In
order to take into account the information embedded in the hierarchy, weight
factors are used to take into account the importance of each level. An
acceptance/rejection strategy in the top-down decision making process is
proposed, which improves the overall categorization accuracy by rejecting a few
percentage of samples, i.e., those with low reliability score. Experimental
results on the Reuters benchmark dataset (RCV1- v2) confirm the effectiveness
of the proposed method, compared to other state-of-the art HTC methods
An Intelligent System For Arabic Text Categorization
Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. In this paper, an intelligent Arabic text categorization system is presented. Machine learning algorithms are used in this system. Many algorithms for stemming and feature selection are tried. Moreover, the document is represented using several term weighting schemes and finally the k-nearest neighbor and Rocchio classifiers are used for classification process. Experiments are performed over self collected data corpus and the results show that the suggested hybrid method of statistical and light stemmers is the most suitable stemming algorithm for Arabic language. The results also show that a hybrid approach of document frequency and information gain is the preferable feature selection criterion and normalized-tfidf is the best weighting scheme. Finally, Rocchio classifier has the advantage over k-nearest neighbor classifier in the classification process. The experimental results illustrate that the proposed model is an efficient method and gives generalization accuracy of about 98%
Machine Learning in Automated Text Categorization
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 problem
is based on machine learning techniques: a general inductive process
automatically builds a classifier by learning, from a set of preclassified
documents, the characteristics of the categories. The advantages of this
approach over the knowledge engineering approach (consisting in the manual
definition of a classifier by domain experts) are a very good effectiveness,
considerable savings in terms of expert manpower, and straightforward
portability to different domains. This survey discusses the main approaches to
text categorization that fall within the machine learning paradigm. We will
discuss in detail issues pertaining to three different problems, namely
document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey
An Investigation into the Pedagogical Features of Documents
Characterizing the content of a technical document in terms of its learning
utility can be useful for applications related to education, such as generating
reading lists from large collections of documents. We refer to this learning
utility as the "pedagogical value" of the document to the learner. While
pedagogical value is an important concept that has been studied extensively
within the education domain, there has been little work exploring it from a
computational, i.e., natural language processing (NLP), perspective. To allow a
computational exploration of this concept, we introduce the notion of
"pedagogical roles" of documents (e.g., Tutorial and Survey) as an intermediary
component for the study of pedagogical value. Given the lack of available
corpora for our exploration, we create the first annotated corpus of
pedagogical roles and use it to test baseline techniques for automatic
prediction of such roles.Comment: 12th Workshop on Innovative Use of NLP for Building Educational
Applications (BEA) at EMNLP 2017; 12 page
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