Abstract
Evaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to assess single words lexical complexity, combining linguistic and psycholinguistic variables in a set of experiments involving random forest and XGboost regressors. Beyond encoding out-of-context information about the lemma, we implemented features based on pre-trained language models to model the target word’s in-context complexity.- Anthology ID:
- 2021.semeval-1.90
- 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:
- 694–699
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.90
- DOI:
- 10.18653/v1/2021.semeval-1.90
- Cite (ACL):
- Irene Russo. 2021. archer at SemEval-2021 Task 1: Contextualising Lexical Complexity. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 694–699, Online. Association for Computational Linguistics.
- Cite (Informal):
- archer at SemEval-2021 Task 1: Contextualising Lexical Complexity (Russo, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.90.pdf
- Data
- Visual Genome