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CompNA at SemEval-2021 Task 1: Prediction of lexical complexity analyzing heterogeneous features

Contributo in Atti di convegno
Data di Pubblicazione:
2021
Abstract:
This paper describes the CompNa model that has been submitted to the Lexical Complexity Prediction (LCP) shared task hosted at SemEval 2021 (Task 1). The solution is based on combining features of different nature through an ensembling method based on Decision Trees and trained using Gradient Boosting. We discuss the results of the model and highlight the features with more predictive capabilities. ? 2021 Association for Computational Linguistics.
Tipologia CRIS:
04.01 Contributo in Atti di convegno
Keywords:
heterogeneous features; Gradient Boosting; lexical complexity
Elenco autori:
Sorgente, Antonio
Autori di Ateneo:
SORGENTE ANTONIO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/459085
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138948501&partnerID=40&md5=bdf3ac06408543628174722e0d64a7b9
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