Lexical complexity prediction: an overview

North, Kai, Zampieri, Marcos and Shardlow, Matthew ORCID: https://orcid.org/0000-0003-1129-2750 (2023) Lexical complexity prediction: an overview. ACM Computing Surveys, 55 (9). p. 179. ISSN 0360-0300

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Abstract

The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modeling has been applied to identify complex words in texts and substitute them for simpler alternatives. In this article, 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.