nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Image and Signal Processing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Review: Automatic Image Annotation for Semantic Image Retrieval

Nächstes Kapitel Automatic Anonymization of Printed-Text Document Images

Zaki, T.: Indexation par le contenu et archivage de fonds documentaires arabes. Thesis. Ibn Zohr University, Agadir, Morocco (2013)

Thabtah F., Hadi, W., Al-shammare, G.: VSMs with K-nearestneighbour to categorise Arabic text data. In: Proceedings of The World Congress on Engineering and Computer Science, WCECS 2008, pp. 778–781 (2008)

Mohamed, R., Watada, J.: An evidential reasoning basedlsa approach to document classification for knowledge acquisition. In: Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2010, pp. 1092–1096. Institute of Electrical and Electronics Engineers (IEEE) (2010)

Zaki, T., Mammass, D., Ennaji, A.: A semantic proximity based system of arabic text indexation. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D., Meunier, J. (eds.) ICISP 2010. LNCS, vol. 6134, pp. 419–427. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13681-8_49CrossRef

Al-Shalabi, R., Obeidat, R.: Improving KNN arabic text classification with n-grams based document indexing. In: Proceedings of the Sixth International Conference on Informatics and Systems, INFOS q 2008, pp. 108–112 (2008)

Jamoussi, S.: Une nouvelle représentation vectorielle pour la classification sémantique. TAL 2009, vol. 50 (2009)

Zaki, T., Mammass, D., Ennaji, A., Nicolas, S.: A kernel hybridization NGram-Okapi for indexing and classification of Arabic documents. J. Inf. Comput. Sci. 9(2), 141–153 (2014). ISSN 1746-7659, England, UK

Mesleh, A.M., Kanaan, G.: Support vector machine text classification system: using ant colony optimization based feature subset selection. In: Proceeding of the International Conference on Computer Engineering & Systems, ICCES 2008, pp. 143–148 (2008)

Hasan, K.S., Ng, V.: Conundrums in unsupervised keyphrases extraction: making sense of the state of the art. In: Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), Poster Volume (2010)

10.

Mesleh, A.: Support vector machines based Arabic language text classification system : feature selection comparative study. In: Proceedings of the 12th WSEAS International Conference on Applied Mathematics, MATHq 2007, pp. 11–16. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA (2007)CrossRef

11.

Hofmann, T.: Probabilistic latent semantic indexing. In: SIGIR 1999 Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57 (1999)

12.

Yang, Y., Chute, G.C.: An example-based mapping method for text categorization and retrieval. ACM Trans. Inf. Syst. 12(3), 252–277 (1994)CrossRef

13.

Kanaan, G., Al-Shalabi, R., AL-Akhras, A.: KNN Arabic text categorization using IG feature selection. In: Proceedings of The 4th International Multiconference on Computer Science and Information Technology, CSIT 2006, vol. 4 (2006)

14.

Li, H.Y., Jain, K.A.: Classification of text documents. Comput. J. 41(8), 537–546 (1998)CrossRef

15.

El-Halees, A.M.: Arabic text classification using maximum entropy. Islam. Univ. J. (Ser. Nat. Stud. Eng.) 15(1), 157–167 (2007)

16.

Duwairi, R.M.: A distance-based classifier for Arabic text categorization. In: Proceedings of The 2005 International Conference on Data Mining, DMIN 2005, pp. 187–192. CSREA Press (2005)

17.

Khreisat, L.: Arabic text classification using N-gram frequency statistics a comparative study. In: Proceedings of The 2006 International Conference on Data Mining, DMIN 2006, pp. 78–82. CSREA Press (2006)

18.

Benkhalifa, M.A., Mouradi, A., Bouyakhf, H.: Integrating WordNet knowledge to supplement training data in semi-supervised agglomerative hierarchical clustering for text categorization. Int. J. Intell. Syst. 16(8), 929–947 (2001)CrossRef

19.

Motasem, A., Joseph, D.: « Levée d’ambigüité par la méthode d’exploration contextuelle: la séquence’alif-nûn (نا) en arabe » , In: Ghenima, M., Ouksel, A., Sidhom, S. (eds.) Systèmes d’Information et Intelligence Economique, 2ème Conférence Internationale (SIIE 2009), organisée par l’université de Nancy, France et l’université de la Manouba, École supérieure de commerce électronique (ESCE), Tunis, Tunis, Hammamet, 12–14 février 2009, IHE éditions, pp. 573–585 (2009)

20.

Mihalcea, R., Tarau, P.: Textrank: bringing order into texts. In: Proceedings of EMNLP, pp. 404–411 (2004)

21.

Page, L., Brin, L., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998)

22.

Al-Shalabi, R., Kanaan, G., Gharaibeh, M.: Arabic text categorization using kNN algorithm. In: Proceedings of the 6th International Conference on Advanced Information Management and Service, IMS 2010. Institute of Electrical and Electronics Engineers (IEEE) (2010)

Titel: Graph-Based Text Modeling: Considering Mathematical Semantic Linking to Improve the Indexation of Arabic Documents
verfasst von: Mohamed Salim El Bazzi
Driss Mammass
Taher Zaki
Abdelatif Ennaji
Verlag: Springer International Publishing
Buch: Image and Signal Processing
Print ISBN: 978-3-319-94210-0

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

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-94211-7_16

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"