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

Giuseppe Vettigli, Antonio Sorgente


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 ensambling 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.
Anthology ID:
2021.semeval-1.69
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
560–564
Language:
URL:
https://aclanthology.org/2021.semeval-1.69
DOI:
10.18653/v1/2021.semeval-1.69
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.69.pdf