@inproceedings{liebeskind-etal-2021-jct,
    title = "{JCT} at {S}em{E}val-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction",
    author = "Liebeskind, Chaya  and
      Elkayam, Otniel  and
      Liebeskind, Shmuel",
    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/landing_page/2021.semeval-1.13/",
    doi = "10.18653/v1/2021.semeval-1.13",
    pages = "138--143",
    abstract = "In 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"
}