Multi-class Text Complexity Evaluation via Deep Neural Networks

Giosue' Lo Bosco, Alfredo Cuzzocrea, Giovanni Pilato, Daniele Schicchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results show that a higher detail level of the classification makes the ATE problem much harder to resolve, showing the weaknesses of the model to accomplish the task correctly.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2019, 20th International Conference Manchester, UK, November 14–16, 2019 Proceedings, Part II
Number of pages10
Publication statusPublished - 2019

Publication series


All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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