Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity Using Word Embeddings

Abstract

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.