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A recurrent deep neural network model to measure sentence complexity for the Italian Language

Academic Article
Publication Date:
2019
abstract:
Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS. We have also provided a comparison of our model with a state of the art method used for the same purpose.
Iris type:
01.01 Articolo in rivista
Keywords:
Automatic Text Complexity Evaluation; Deep Neural Networks; NLP
List of contributors:
Pilato, Giovanni
Authors of the University:
PILATO GIOVANNI
Handle:
https://iris.cnr.it/handle/20.500.14243/383289
Published in:
CEUR WORKSHOP PROCEEDINGS
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http://www.scopus.com/record/display.url?eid=2-s2.0-85071309608&origin=inward
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