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Indexing of Arabic documents automatically based on lexical analysis
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TLDR
This paper proposed and implemented a method to automatically create and Index for books written in Arabic language and depends largely on text summarization.Abstract:
The continuous information explosion through the Internet and all information sources makes it necessary to perform all information processing activities automatically in quick and reliable manners. In this paper, we proposed and implemented a method to automatically create and Index for books written in Arabic language. The process depends largely on text summarization andread more
Citations
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Journal ArticleDOI
Arabic Text Classification Algorithm using TFIDF and Chi Square Measurements
TL;DR: A new method for Arabic text classification is proposed in which a document is compared with pre-defined documents categories based on its contents using the TF.IDF method, then the document is classified into the appropriate sub-category using Chi Square measure.
Journal ArticleDOI
Different Classification Algorithms Based on Arabic Text Classification: Feature Selection Comparative Study
TL;DR: This paper has compared the performance between different classifiers in different situations using feature selection with stemming, and without stemming, to investigate the effectiveness of use of feature selection.
Journal ArticleDOI
Linked open data of bibliometric networks: analytics research for personalized library services
TL;DR: Experimental analysis shows that topic specificity and citation count of publication venues are negatively correlated to each other, the first attempt to discover correlation between topic sensitivity and citation counts of publication venue.
Proceedings ArticleDOI
Stemming versus multi-words indexing for Arabic documents classification
TL;DR: Empirical results on Arabic dataset reveal that the choice of extracted feature's type has a significant impact on conserving semantic information and improving classification accuracy, especially with the morphological complexity of the Arabic language.
Book ChapterDOI
Concatenation Technique for Extracted Arabic Characters for Efficient Content-based Indexing and Searching
TL;DR: This research paper demonstrates the work accomplished in the last phase of the ongoing research project with an objective of developing a system for moving Arabic video text extraction for efficient content-based indexing and searching.
References
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Book
Modern Information Retrieval
TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.
Journal ArticleDOI
An improved K-nearest-neighbor algorithm for text categorization
TL;DR: An improved KNN algorithm is proposed, which builds the classification model by combining constrained one pass clustering algorithm and KNN text categorization, which can reduce the text similarity computation substantially and outperform the-state-of-the-art KNN, Naive Bayes and Support Vector Machine classifiers.
Journal Article
Arabic Text Classification Using Maximum Entropy
TL;DR: In this article, the authors focused on classifying Arabic text documents and used a maximum entropy method to classify Arabic documents, they experimented their approach using real data, then compared the results with other existing systems.
Automated Arabic Text Categorization Using SVM and NB
TL;DR: The Experimental results against different Arabic text categorization data sets reveal that SVM algorithm outperforms the Naive Bayesian method (NB) with regards to all measures.
Posted Content
An Improved k-Nearest Neighbor Algorithm for Text Categorization
Baoli Li,Shiwen Yu,Qin Lu +2 more
TL;DR: An improved kNN algorithm is proposed, which uses different numbers of nearest neighbors for different categories, rather than a fixed number across all categories, and is promising for some cases, where estimating the parameter k via cross-validation is not allowed.