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 Daniele, Pilato Giovanni, Lo Bosco Giosue' (2020). Deep neural attention-based model for the evaluation of italian sentences complexity. In 14th IEEE International Conference on Semantic Computing ICSC 2020 (pp. 253-256). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/ICSC.2020.00053].

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

Schicchi Daniele
;
Pilato Giovanni;Lo Bosco Giosue'
2020-01-01

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.
2020
Settore INF/01 - Informatica
978-1-7281-6332-1
Schicchi Daniele, Pilato Giovanni, Lo Bosco Giosue' (2020). Deep neural attention-based model for the evaluation of italian sentences complexity. In 14th IEEE International Conference on Semantic Computing ICSC 2020 (pp. 253-256). 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/ICSC.2020.00053].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/580038
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