  The occurrence of unknown words in texts significantly hinders reading
comprehension. To improve accessibility for specific target populations,
computational modelling has been applied to identify complex words in texts and
substitute them for simpler alternatives. In this paper, we present an overview
of computational approaches to lexical complexity prediction focusing on the
work carried out on English data. We survey relevant approaches to this problem
which include traditional machine learning classifiers (e.g. SVMs, logistic
regression) and deep neural networks as well as a variety of features, such as
those inspired by literature in psycholinguistics as well as word frequency,
word length, and many others. Furthermore, we introduce readers to past
competitions and available datasets created on this topic. Finally, we include
brief sections on applications of lexical complexity prediction, such as
readability and text simplification, together with related studies on languages
other than English.
