@inproceedings{agarwal-chatterjee-2021-langresearchlab-nc,
    title = "{L}ang{R}esearch{L}ab {NC} at {S}em{E}val-2021 Task 1: Linguistic Feature Based Modelling for Lexical Complexity",
    author = "Agarwal, Raksha  and
      Chatterjee, Niladri",
    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/fix-sig-urls/2021.semeval-1.10/",
    doi = "10.18653/v1/2021.semeval-1.10",
    pages = "120--125",
    abstract = "The present work aims at assigning a complexity score between 0 and 1 to a target word or phrase in a given sentence. For each Single Word Target, a Random Forest Regressor is trained on a feature set consisting of lexical, semantic, and syntactic information about the target. For each Multiword Target, a set of individual word features is taken along with single word complexities in the feature space. The system yielded the Pearson correlation of 0.7402 and 0.8244 on the test set for the Single and Multiword Targets, respectively."
}