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2021 SemEval SemEval 2021

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

Abstract

AbstractWe 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.

Authors

Regina Stodden, Gayatri Venugopal

Topics

Machine Learning > Core Methods > Regression Machine Learning > Core Methods > Representation Learning Deep Learning > Architectures > Neural Networks

Keywords

word embedding character embedding neural network lexical complexity prediction hand-crafted linguistic feature

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