In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.

Schicchi D., Pilato G., & Lo Bosco G. (2020). Deep neural attention-based model for the evaluation of italian sentences complexity. In Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020 (pp. 253-256). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSC.2020.00053].

Deep neural attention-based model for the evaluation of italian sentences complexity

Schicchi D.;Pilato G.;Lo Bosco G.
2020

Abstract

In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
978-1-7281-6332-1
Schicchi D., Pilato G., & Lo Bosco G. (2020). Deep neural attention-based model for the evaluation of italian sentences complexity. In Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020 (pp. 253-256). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSC.2020.00053].
File in questo prodotto:
File Dimensione Formato  
09031472.pdf

Solo gestori archvio

Tipologia: Versione Editoriale
Dimensione 106.94 kB
Formato Adobe PDF
106.94 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/410099
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 3
social impact