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 t
he 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.
We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021. The approach consists of separating sentences into smaller chunks, embedding them with Sent2Vec, and reducing the em
beddings into a simpler vector used as input to a neural network, the latter for predicting the complexity of words and expressions. Results show that the pre-trained sentence embeddings are not able to capture lexical complexity from the language when applied in cross-domain applications.
