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2021 SemEval SemEval 2021

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

Abstract

AbstractThis 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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

Authors

Giuseppe Vettigli, Antonio Sorgente

Topics

Machine Learning > Core Methods > Regression Natural Language Processing > Understanding > Semantic Analysis Natural Language Processing > Applications > Text Classification

Keywords

ensemble learning lexical complexity semantic analysis gradient boosting feature engineering decision tree ensemble
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