@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}