  This paper describes team LCP-RIT's submission to the SemEval-2021 Task 1:
Lexical Complexity Prediction (LCP). The task organizers provided participants
with an augmented version of CompLex (Shardlow et al., 2020), an English
multi-domain dataset in which words in context were annotated with respect to
their complexity using a five point Likert scale. Our system uses logistic
regression and a wide range of linguistic features (e.g. psycholinguistic
features, n-grams, word frequency, POS tags) to predict the complexity of
single words in this dataset. We analyze the impact of different linguistic
features in the classification performance and we evaluate the results in terms
of mean absolute error, mean squared error, Pearson correlation, and Spearman
correlation.
