Lexical Complexity Prediction: An Overview

Understanding the meaning of words in context is fundamental for reading comprehension. The perceived difficulty, hereafter referred to as complexity, of a target word within a given text varies widely among readers. With an increased demand for distance learning and educational technologies[107], research into automatically predicting which words are likely to cause comprehension problems is becoming a popular area of research [115, 147, 185]. Systems have been created to identify complex words that are difficult to acquire, reproduce, or understand for children [79], second-language learners [89], people suffering from a reading disability, such as dyslexia [131] or aphasia [35, 53], or more generally, individuals with low literacy [59, 175]. In Computational Linguistics and Natural Language Processing (NLP), the task of automatically recognizing complex words is most often achieved by training machine learning (ML) models. These ML models assign a complexity value to each target word within an inputted extract, sentence, or text that allows for the identification of complex words. This information can then be used to improve downstream lexical and text simplification systems that provide simpler alternatives to aid reading comprehension. Take the extract shown in Table 1 for example.