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2021 SemEval SemEval 2021

JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction

Abstract

AbstractIn this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words

Authors

Chaya Liebeskind, Otniel Elkayam, Shmuel Liebeskind

Topics

Natural Language Processing > Applications > Text Classification Natural Language Processing > Resources & Methods > Text Representation

Keywords

lexical complexity semantic analysis machine learning feature integration contextualized word embedding
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