Document (#42292)

Author

Cavalcante Dourado, Í.
Galante, R.
Gonçalves, M.A.
Silva Torres, R. de

Title

Bag of textual graphs (BoTG) : a general graph-based text representation model

Source

Journal of the Association for Information Science and Technology. 70(2019) no.8, S.817-829

Year

2019

Abstract

Text representation models are the fundamental basis for information retrieval and text mining tasks. Although different text models have been proposed, they typically target specific task aspects in isolation, such as time efficiency, accuracy, or applicability for different scenarios. Here we present Bag of Textual Graphs (BoTG), a general text representation model that addresses these three requirements at the same time. The proposed textual representation is based on a graph-based scheme that encodes term proximity and term ordering, and represents text documents into an efficient vector space that addresses all these aspects as well as provides discriminative textual patterns. Extensive experiments are conducted in two experimental scenarios-classification and retrieval-considering multiple well-known text collections. We also compare our model against several methods from the literature. Experimental results demonstrate that our model is generic enough to handle different tasks and collections. It is also more efficient than the widely used state-of-the-art methods in textual classification and retrieval tasks, with a competitive effectiveness, sometimes with gains by large margins.

Content

Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24167.

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4. Zhang, W.; Yoshida, T.; Tang, X.: ¬A comparative study of TF*IDF, LSI and multi-words for text classification (2011) 0.30

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5. Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Xiangji Huang, J.; Ben Jemaa, M.: MF-Re-Rank : a modality feature-based re-ranking model for medical image retrieval (2018) 0.30

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