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ViStA: a visualization system for exploring Arabic text

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Abstract

Text visualization has become a significant tool that facilitates knowledge discovery and insightful presentation of large amounts of data. This paper presents a visualization system for exploring Arabic text called ViStA. We report about the design, the implementation and some of the experiments we conducted on the system. The development of such tools assists Arabic language analysts to effectively explore, understand, and discover interesting knowledge hidden in text data. We used statistical techniques from the field of Information Retrieval to identify the relevant documents coupled with sophisticated natural language processing (NLP) tools to process the text. For text visualization, the system used a hybrid approach combining latent semantic indexing for feature selection and multidimensional scaling for dimensionality reduction. Initial results confirm the viability of using this approach to tackle the problem of Arabic text visualization and other Arabic NLP applications.

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Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. King Abdullah II School for Informational Technology, The University of Jordan, Amman, Jordan

    Bassam Hammo, Nadim Obeid & Israa Huzayyen

Authors
  1. Bassam Hammo
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  2. Nadim Obeid
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  3. Israa Huzayyen
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Corresponding author

Correspondence to Bassam Hammo.

Additional information

This paper was written while the first author was on sabbatical leave from The University of Jordan to Princess Sumaya University of Technology (PSUT), Amman-Jordan.

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Hammo, B., Obeid, N. & Huzayyen, I. ViStA: a visualization system for exploring Arabic text. Int J Speech Technol 19, 237–247 (2016). https://doi.org/10.1007/s10772-015-9286-4

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