Complex words identification using word-level features for SemEval-2020 Task 1

Jenny A. Ortiz-Zambrano, Arturo Montejo-Ráez


Abstract
This article describes a system to predict the complexity of words for the Lexical Complexity Prediction (LCP) shared task hosted at SemEval 2021 (Task 1) with a new annotated English dataset with a Likert scale. Located in the Lexical Semantics track, the task consisted of predicting the complexity value of the words in context. A machine learning approach was carried out based on the frequency of the words and several characteristics added at word level. Over these features, a supervised random forest regression algorithm was trained. Several runs were performed with different values to observe the performance of the algorithm. For the evaluation, our best results reported a M.A.E score of 0.07347, M.S.E. of 0.00938, and R.M.S.E. of 0.096871. Our experiments showed that, with a greater number of characteristics, the precision of the classification increases.
Anthology ID:
2021.semeval-1.11
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:
126–129
Language:
URL:
https://aclanthology.org/2021.semeval-1.11
DOI:
10.18653/v1/2021.semeval-1.11
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.11.pdf