ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources

Armand Rotaru


Abstract
In this paper we describe our participation in the Lexical Complexity Prediction (LCP) shared task of SemEval 2021, which involved predicting subjective ratings of complexity for English single words and multi-word expressions, presented in context. Our approach relies on a combination of distributional models, both context-dependent and context-independent, together with behavioural norms and lexical resources.
Anthology ID:
2021.semeval-1.84
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:
655–660
Language:
URL:
https://aclanthology.org/2021.semeval-1.84
DOI:
10.18653/v1/2021.semeval-1.84
Bibkey:
Cite (ACL):
Armand Rotaru. 2021. ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 655–660, Online. Association for Computational Linguistics.
Cite (Informal):
ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources (Rotaru, SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.84.pdf
Code
 armandrotaru/teamandi-lcp
Data
ConceptNet