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59
AN IMPROVED ARABIC WORD’S ROOTS EXTRACTION METHOD USING N-GRAM TECHNIQUE
"... Arabic language is distinguished by its morphological richness, which forces the workers in the field of Arabic language Processing (i.e., information retrieval, document’s classification, text summarizing) to deal with many words that seem to be different but in reality they came from an identical ..."
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Cited by 1 (1 self)
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root word. One of the methods to overcome this problem is to return the words to their roots. This research aims to provide a new algorithm, that returns roots of Arabic words using n-gram technique without using morphological rules in order to avoid the complexity arising from the morphological
Text Classification Improved through Multigram Models
- In Proceedings of the ACM Fifteenth Conference on Information and Knowledge Management (CIKM'06
"... Classification algorithms and document representation approaches are two key elements for a successful document classification system. In the past, much work has been conducted to find better ways to represent documents. However, most of the attempts rely on certain extra resources such as WordNet, ..."
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Cited by 3 (0 self)
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language models, we put forward two text classification algorithms. The experiments on RCV1 show that our proposed algorithm based on n-multigram models alone can achieve the similar or even better classification performance compared with the classifier based on n-gram models but the model size of our
Combinatorial approach for Text Classification Algorithm
"... Abstract — There are large number of websites, web portals available in market places in order to buy or sell objects that means any kind of products. For examples filpkart, ebay, Amazon etc. due increase number of this web portals as well as its application Text mining, integration is become import ..."
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there category. In this paper we used powerful method based on parallel text classification. To attack above problem availability of source data or document could help to find out better prediction. We formulated above problem to the best from our knowledge and study we showed classifier with parallel approach
Concept indexing: A fast dimensionality reduction algorithm with applications to document retrieval and categorization
- IN CIKM’00
, 2000
"... In recent years, we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. This has led to an increased interest in developing meth-ods that can efficiently categorize and retrieve relevant information. Re ..."
Abstract
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Cited by 81 (5 self)
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In recent years, we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. This has led to an increased interest in developing meth-ods that can efficiently categorize and retrieve relevant information
Sentiment Mining for Natural Language Documents Wikipedia-based Text Classification
, 2009
"... The Internet and even our personal computers are full of unlabeled text content and we could benefit from tools that automatically organize and tag this text content so that we can quickly access the appropriate content when needed. Supervised learning algorithms for text classification that learn f ..."
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The Internet and even our personal computers are full of unlabeled text content and we could benefit from tools that automatically organize and tag this text content so that we can quickly access the appropriate content when needed. Supervised learning algorithms for text classification that learn
A Feature Weight Adjustment Algorithm for Document Categorization
, 2000
"... In recent years we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intra-nets. Automatic text categorization, which is the task of assigning text documents to pre-specified classes (topics or themes) of docume ..."
Abstract
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Cited by 11 (1 self)
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In recent years we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intra-nets. Automatic text categorization, which is the task of assigning text documents to pre-specified classes (topics or themes
Pruning Non-Informative Text Through Non-Expert Annotations to Improve Aspect-Level Sentiment Classification. COLING
, 2010
"... Sentiment analysis attempts to extract the author’s sentiments or opinions from unstructured text. Unlike approaches based on rules, a machine learning approach holds the promise of learning robust, highcoverage sentiment classifiers from labeled examples. However, people tend to use different ways ..."
Abstract
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Cited by 3 (0 self)
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-sentiment information cannot be identified as noise by the learning algorithm and can easily become correlated with the sentiment label, thereby confusing sentiment classifiers. In this paper, we present a highly effective procedure for using crowd-sourcing techniques to label informative and non
A Detailed Study on Text Mining using Genetic Algorithm
"... Abstract-- The text mining studies are gaining more importance recently because of the availability of the increasing number of the electronic documents from a variety of sources. Few examines direct on applying Genetic Algorithm to text classification, summarization and information retrieval system ..."
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Abstract-- The text mining studies are gaining more importance recently because of the availability of the increasing number of the electronic documents from a variety of sources. Few examines direct on applying Genetic Algorithm to text classification, summarization and information retrieval
Soft indexing of speech content for search in spoken documents
- Computer Speech and Language
"... Abstract The paper presents the Position Specific Posterior Lattice (PSPL), a novel lossy representation of automatic speech recognition lattices that naturally lends itself to efficient indexing and subsequent relevance ranking of spoken documents. This technique explicitly takes into consideratio ..."
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Cited by 18 (2 self)
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into consideration the content uncertainty by means of using soft-hits. Indexing position information allows one to approximate N-gram expected counts and at the same time use more general proximity features in the relevance score calculation. In fact, one can easily port any state-of-the-art text
Semantic Smoothing for Text Clustering
"... In this paper we present a new semantic smoothing vector space kernel (S-VSM) for text documents clustering. In the suggested approach semantic relatedness between words is used to smooth the similarity and the represen-tation of text documents. The basic hypothesis examined is that considering sema ..."
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semantic relatedness between two text documents may improve the perfor-mance of the text document clustering task. For our experimental evaluation we analyze the performance of several semantic relatedness measures when embedded in the proposed (S-VSM) and present results with respect to dif
Results 1 - 10
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