@inproceedings{stodden-venugopal-2021-rs,
title = "{RS}{\_}{GV} at {S}em{E}val-2021 Task 1: Sense Relative Lexical Complexity Prediction",
author = "Stodden, Regina and
Venugopal, Gayatri",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.82",
doi = "10.18653/v1/2021.semeval-1.82",
pages = "640--649",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction
%A Stodden, Regina
%A Venugopal, Gayatri
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F stodden-venugopal-2021-rs
%X 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.
%R 10.18653/v1/2021.semeval-1.82
%U https://aclanthology.org/2021.semeval-1.82
%U https://doi.org/10.18653/v1/2021.semeval-1.82
%P 640-649
Markdown (Informal)
[RS_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction](https://aclanthology.org/2021.semeval-1.82) (Stodden & Venugopal, SemEval 2021)
ACL