Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings
Erik Rozi, Niveditha Iyer, Gordon Chi, Enok Choe, Kathy J. Lee, Kevin Liu, Patrick Liu, Zander Lack, Jillian Tang, Ethan A. Chi
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This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction. Text comprehension depends on the reader's ability to understand the words present in it; evaluating the lexical complexity of such texts can enable readers to find an appropriate text and systems to tailor a text to an audience's needs. We present our model pipeline, which applies a combination of embedding-based and manual features to predict lexical complexity on the CompLex English dataset using various tree-based and linear models. Our method is ranked 27 / 54 on single-word prediction and 14 / 37 on multi-word prediction.