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EnglishIn this paper we present a deep neural network model capable of inducing the rules that identify the syntax complexity of an Italian sentence. Our system, beyond the ability of choosing if a sentence needs of simplification, gives a score that represent the confidence of the model during the process of decision making which could be representative of the sentence complexity. Experiments have been carried out on one public corpus created specifically for the problem of text-simplification.
Lo Bosco, G., Pilato, G., & Schicchi, D. (2018). A sentence based system for measuring syntax complexity using a recurrent deep neural network. In CEUR Workshop Proceedings (pp. 95-101). CEUR.
| Data di pubblicazione: | 2018 |
| Titolo: | A sentence based system for measuring syntax complexity using a recurrent deep neural network |
| Autori: | |
| Citazione: | Lo Bosco, G., Pilato, G., & Schicchi, D. (2018). A sentence based system for measuring syntax complexity using a recurrent deep neural network. In CEUR Workshop Proceedings (pp. 95-101). CEUR. |
| Abstract: | In this paper we present a deep neural network model capable of inducing the rules that identify the syntax complexity of an Italian sentence. Our system, beyond the ability of choosing if a sentence needs of simplification, gives a score that represent the confidence of the model during the process of decision making which could be representative of the sentence complexity. Experiments have been carried out on one public corpus created specifically for the problem of text-simplification. |
| URL: | http://ceur-ws.org/ |
| Appare nelle tipologie: | 2.07 Contributo in atti di convegno pubblicato in volume |
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