# Lexical Complexity Prediction: An Overview

arXiv: https://arxiv.org/abs/2303.04851 · HTML: https://mlsanity.com/paper/2303.04851/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 198 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification](https://mlsanity.com/paper/1810.05754/) (2018)
- [CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data](https://mlsanity.com/paper/2003.07008/) (2020)
- [On the Ethical Considerations of Text Simplification](https://mlsanity.com/paper/2204.09565/) (2022)
- [SemEval-2021 Task 1: Lexical Complexity Prediction](https://mlsanity.com/paper/2106.00473/) (2021)
- [Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset](https://mlsanity.com/paper/2102.08773/) (2021)
- [A Survey on Text Simplification](https://mlsanity.com/paper/2008.08612/) (2020)
- [Strong Baselines for Complex Word Identification across Multiple Languages](https://mlsanity.com/paper/1904.05953/) (2019)
- [Complex Word Identification: Challenges in Data Annotation and System Performance](https://mlsanity.com/paper/1710.04989/) (2017)
- [Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments](https://mlsanity.com/paper/1812.09628/) (2018)
- [UnibucKernel: A kernel-based learning method for complex word identification](https://mlsanity.com/paper/1803.07602/) (2018)
- [Cross-Lingual Transfer Learning for Complex Word Identification](https://mlsanity.com/paper/2010.01108/) (2020)
- [UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction](https://mlsanity.com/paper/2104.06983/) (2021)
- [IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task](https://mlsanity.com/paper/2104.01046/) (2021)
- [LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction](https://mlsanity.com/paper/2105.08780/) (2021)
- [CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification](https://mlsanity.com/paper/1709.02843/) (2017)
- [BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models](https://mlsanity.com/paper/2104.09040/) (2021)
- [LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures](https://mlsanity.com/paper/2105.09653/) (2021)

## Built on by (most cited first, 3 of 3)

- [Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques](https://mlsanity.com/paper/2506.06579/) (2025)
- [Deep Learning Approaches to Lexical Simplification: A Survey](https://mlsanity.com/paper/2305.12000/) (2023)
- [Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning](https://mlsanity.com/paper/2401.15043/) (2024)

## Abstract (arXiv)

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.