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
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- 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
- Cite (ACL):
- Jenny A. Ortiz-Zambrano and Arturo Montejo-Ráez. 2021. Complex words identification using word-level features for SemEval-2020 Task 1. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 126–129, Online. Association for Computational Linguistics.
- Cite (Informal):
- Complex words identification using word-level features for SemEval-2020 Task 1 (Ortiz-Zambrano & Montejo-Ráez, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.11.pdf