← Back to papers
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 — Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

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
Download PDF

Related papers

UIT-ISE-NLP at SemEval-2021 Task 5: Toxic Spans Detection with BiLSTM-CRF and ToxicBERT Comment Classification 2021
BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data 2021
Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation 2021
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way 2021
katildakat at SemEval-2021 Task 1: Lexical Complexity Prediction of Single Words and Multi-Word Expressions in English 2021