Abstract
We present the technical report of the system called RS_GV at SemEval-2021 Task 1 on lexical complexity prediction of English words. RS_GV is a neural network using hand-crafted linguistic features in combination with character and word embeddings to predict target words’ complexity. For the generation of the hand-crafted features, we set the target words in relation to their senses. RS_GV predicts the complexity well of biomedical terms but it has problems with the complexity prediction of very complex and very simple target words.- Anthology ID:
- 2021.semeval-1.82
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 640–649
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.82
- DOI:
- 10.18653/v1/2021.semeval-1.82
- Cite (ACL):
- Regina Stodden and Gayatri Venugopal. 2021. RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 640–649, Online. Association for Computational Linguistics.
- Cite (Informal):
- RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction (Stodden & Venugopal, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.semeval-1.82.pdf