DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages

Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibi...

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Autores principales:	Giosué Lo Bosco, Giovanni Pilato, Daniele Schicchi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Text-complexity-assessment Automatic-text-complexity-evaluation Text-simplification Artificial-intelligence Deep-learning Natural-language-processing Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/7e36055a1d474631b4ad6e3b116f8379
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spelling	oai:doaj.org-article:7e36055a1d474631b4ad6e3b116f83792021-12-02T05:03:33ZDeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages2590-005610.1016/j.array.2021.100097https://doaj.org/article/7e36055a1d474631b4ad6e3b116f83792021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2590005621000424https://doaj.org/toc/2590-0056Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named DeepEva, a Deep Learning based system capable of classifying both Italian and English sentences on the basis of their complexity. The system exploits the Treetagger annotation tool, two Long Short Term Memory (LSTM) neural unit layers, and a fully connected one. The last layer outputs the probability of a sentence belonging to the easy or complex class. The experimental results show the effectiveness of the approach for both languages, compared with several baselines such as Support Vector Machine, Gradient Boosting, and Random Forest.Giosué Lo BoscoGiovanni PilatoDaniele SchicchiElsevierarticleText-complexity-assessmentAutomatic-text-complexity-evaluationText-simplificationArtificial-intelligenceDeep-learningNatural-language-processingComputer engineering. Computer hardwareTK7885-7895Electronic computers. Computer scienceQA75.5-76.95ENArray, Vol 12, Iss , Pp 100097- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Text-complexity-assessment Automatic-text-complexity-evaluation Text-simplification Artificial-intelligence Deep-learning Natural-language-processing Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Text-complexity-assessment Automatic-text-complexity-evaluation Text-simplification Artificial-intelligence Deep-learning Natural-language-processing Computer engineering. Computer hardware TK7885-7895 Electronic computers. Computer science QA75.5-76.95 Giosué Lo Bosco Giovanni Pilato Daniele Schicchi DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
description	Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named DeepEva, a Deep Learning based system capable of classifying both Italian and English sentences on the basis of their complexity. The system exploits the Treetagger annotation tool, two Long Short Term Memory (LSTM) neural unit layers, and a fully connected one. The last layer outputs the probability of a sentence belonging to the easy or complex class. The experimental results show the effectiveness of the approach for both languages, compared with several baselines such as Support Vector Machine, Gradient Boosting, and Random Forest.
format	article
author	Giosué Lo Bosco Giovanni Pilato Daniele Schicchi
author_facet	Giosué Lo Bosco Giovanni Pilato Daniele Schicchi
author_sort	Giosué Lo Bosco
title	DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
title_short	DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
title_full	DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
title_fullStr	DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
title_full_unstemmed	DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
title_sort	deepeva: a deep neural network architecture for assessing sentence complexity in italian and english languages
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7e36055a1d474631b4ad6e3b116f8379
work_keys_str_mv	AT giosuelobosco deepevaadeepneuralnetworkarchitectureforassessingsentencecomplexityinitalianandenglishlanguages AT giovannipilato deepevaadeepneuralnetworkarchitectureforassessingsentencecomplexityinitalianandenglishlanguages AT danieleschicchi deepevaadeepneuralnetworkarchitectureforassessingsentencecomplexityinitalianandenglishlanguages
_version_	1718400659539099648

DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages

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