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An Image Processing Method Based on Features Selection for Crop Plants and Weeds Discrimination Using RGB Images Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Graph-Based Text Modeling: Considering Mathematical Semantic Linking to Improve the Indexation of Arabic Documents

Authors : Mohamed Salim El Bazzi, Driss Mammass, Taher Zaki, Abdelatif Ennaji

Published in: Image and Signal Processing

Publisher: Springer International Publishing

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Abstract

Indexing unstructured documents aims to build a list of words, or concepts, which will simplify the exploration of their exploration later on. The most used model for text modeling is the Vector Space Model. In spite of the simplicity of this model in its implementation and its wide use in different researches in the field of text mining and information retrieval, it has an important limit, which is ignoring the semantic relation between the different textual units, by considering them as independent. However, there is a more suitable technique in Data Mining to highlight the semantic linkage between text units, which is the graph-based representation. A graph can easily be adapted to the textual data by representing words as a vertex and the relation between them as edges. In this work, we have introduced the graph based modeling of textual document. Thus, we conducted a study about the impact of the choice of the semantic relation between the text units on the indexation of documents. We have validated our results through classification results.

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Metadata
Title
Graph-Based Text Modeling: Considering Mathematical Semantic Linking to Improve the Indexation of Arabic Documents
Authors
Mohamed Salim El Bazzi
Driss Mammass
Taher Zaki
Abdelatif Ennaji
Copyright Year
2018
Book
Image and Signal Processing

Print ISBN: 978-3-319-94210-0

Electronic ISBN: 978-3-319-94211-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-94211-7_16

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