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
Schicchi, D., Lo Bosco, G., Pilato, G. (2019). Machine Learning Models for Measuring Syntax Complexity of English Text. In A.V. Samsonovich (a cura di), Biologically Inspired Cognitive Architectures 2019, Proceedings of the Tenth Annual Meeting of the BICA Society (pp. 449-454). Springer [10.1007/978-3-030-25719-4_59].
Machine Learning Models for Measuring Syntax Complexity of English Text
Schicchi, Daniele;Lo Bosco, Giosué;Pilato, Giovanni
2019-01-01
Abstract
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
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