# CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data

arXiv: https://arxiv.org/abs/2003.07008 · HTML: https://mlsanity.com/paper/2003.07008/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 24 of 24)

- [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)
- [One Size Does Not Fit All: The Case for Personalised Word Complexity Models](https://mlsanity.com/paper/2205.02564/) (2022)
- [Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification](https://mlsanity.com/paper/2305.12463/) (2023)
- [UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction](https://mlsanity.com/paper/2104.06983/) (2021)
- [Japanese Lexical Complexity for Non-Native Readers: A New Dataset](https://mlsanity.com/paper/2306.17399/) (2023)
- [Deep Learning Approaches to Lexical Simplification: A Survey](https://mlsanity.com/paper/2305.12000/) (2023)
- [Difficult for Whom? A Study of Japanese Lexical Complexity](https://mlsanity.com/paper/2410.18567/) (2024)
- [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)
- [Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification](https://mlsanity.com/paper/2205.07283/) (2022)
- [Lexical Complexity Prediction: An Overview](https://mlsanity.com/paper/2303.04851/) (2023)
- [Exploring the Effects of Alignment on Numerical Bias in Large Language Models](https://mlsanity.com/paper/2601.16444/) (2026)
- [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)
- [Multilingual Controllable Transformer-Based Lexical Simplification](https://mlsanity.com/paper/2307.02120/) (2023)
- [Predicting Through Generation: Why Generation Is Better for Prediction](https://mlsanity.com/paper/2502.17817/) (2025)
- [Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning](https://mlsanity.com/paper/2401.15043/) (2024)
- [Lexical Complexity Prediction and Lexical Simplification for Catalan and Spanish: Resource Creation, Quality Assessment, and Ethical Considerations](https://mlsanity.com/paper/2404.07814/) (2024)
- [OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction](https://mlsanity.com/paper/2105.05535/) (2021)
- [cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora](https://mlsanity.com/paper/2106.02340/) (2021)
- [MultiLS: A Multi-task Lexical Simplification Framework](https://mlsanity.com/paper/2402.14972/) (2024)
- [IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual Texts](https://mlsanity.com/paper/2404.04513/) (2024)
- [FlowNIB: An Information Bottleneck Analysis of Bidirectional vs. Unidirectional Language Models](https://mlsanity.com/paper/2506.00859/) (2025)

## Abstract (arXiv)

Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such as text simplification. This task is commonly referred to as Complex Word Identification (CWI). With a few exceptions, previous studies have approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) for a set of target words in a text. This choice is motivated by the fact that all CWI datasets compiled so far have been annotated using a binary annotation scheme. Our paper addresses this limitation by presenting the first English dataset for continuous lexical complexity prediction. We use a 5-point Likert scale scheme to annotate complex words in texts from three sources/domains: the Bible, Europarl, and biomedical texts. This resulted in a corpus of 9,476 sentences each annotated by around 7 annotators.