Search results for "Text-simplification"
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DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
2021
Abstract 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 De…
Machine Learning Models for Measuring Syntax Complexity of English Text
2019
In this paper we propose a methodology to assess the syntax complexity of a sentence representing it as sequence of parts-of-speech and comparing Recurrent Neural Networks and Support Vector Machine. We have carried out experiments in English language which are compared with previous results obtained for the Italian one.