RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction

Regina Stodden, Gayatri Venugopal


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
Venues:
ACL | IJCNLP | 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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.82.pdf