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EnglishThe automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in two different languages: Italian and English.
Schicchi, D., Pilato, G., & Lo Bosco, G. (2020). Attention-based Model for Evaluating the Complexity of Sentences in English Language. In 20TH IEEE MEDITERRANEAN ELETROTECHNICAL CONFERENCE Melecon 2020 (pp. 221-225).
| Data di pubblicazione: | 2020 |
| Titolo: | Attention-based Model for Evaluating the Complexity of Sentences in English Language |
| Autori: | |
| Citazione: | Schicchi, D., Pilato, G., & Lo Bosco, G. (2020). Attention-based Model for Evaluating the Complexity of Sentences in English Language. In 20TH IEEE MEDITERRANEAN ELETROTECHNICAL CONFERENCE Melecon 2020 (pp. 221-225). |
| Abstract: | The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in two different languages: Italian and English. |
| URL: | https://ieeexplore.ieee.org/document/9140531 |
| ISBN: | 978-1-7281-5200-4 |
| Digital Object Identifier (DOI): | 10.1109/MELECON48756.2020.9140531 |
| Settore Scientifico Disciplinare: | Settore INF/01 - Informatica Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni |
| Appare nelle tipologie: | 2.07 Contributo in atti di convegno pubblicato in volume |
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