ANDI at SemEval-2021 Task 1: Predicting complexity in context using distributional models, behavioural norms, and lexical resources
TL
TL;DR
This paper describes the 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.
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.
References28 items
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SemEval-2021 Task 1: Lexical Complexity Prediction
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Predicting lexical complexity in English texts: the Complex 2.0 dataset
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DeBERTa: Decoding-enhanced BERT with Disentangled Attention
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Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis
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