Search results for "Text complexity evaluation"

showing 3 items of 3 documents

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

2020

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.

050101 languages & linguisticsExploitComputer science02 engineering and technologyText complexity evaluationMachine learningcomputer.software_genreTask (project management)Text Simplification0202 electrical engineering electronic engineering information engineering0501 psychology and cognitive sciencesSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniMeasure (data warehouse)Deep Neural NetworksArtificial neural networkSettore INF/01 - Informaticabusiness.industryItalian languageNatural language processing05 social sciencesComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Deep learningText ComplexityBinary classification020201 artificial intelligence & image processingArtificial intelligenceTest phasebusinesscomputerSentence
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Multi-class Text Complexity Evaluation via Deep Neural Networks

2019

Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results sho…

050101 languages & linguisticsSettore INF/01 - InformaticaArtificial neural networkText simplificationbusiness.industryComputer science05 social sciencesText simplification02 engineering and technologyDeep neural networkMachine learningcomputer.software_genreClass (biology)Task (project management)Simple (abstract algebra)Automatic Text Complexity Evaluation0202 electrical engineering electronic engineering information engineeringDeep neural networks020201 artificial intelligence & image processing0501 psychology and cognitive sciencesArtificial intelligencebusinesscomputerScope (computer science)
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A recurrent deep neural network model to measure sentence complexity for the Italian Language

2019

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…

Deep Neural NetworksText Simplification Natural Language Processing Deep Neural NetworksSettore INF/01 - InformaticaComputingMethodologies_DOCUMENTANDTEXTPROCESSINGAutomatic Text Complexity EvaluationNLP
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