# One Size Does Not Fit All: The Case for Personalised Word Complexity Models

arXiv: https://arxiv.org/abs/2205.02564 · HTML: https://mlsanity.com/paper/2205.02564/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 67 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 8 of 8)

- [Learning Active Learning from Data](https://mlsanity.com/paper/1703.03365/) (2017)
- [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)
- [Adaptive Forgetting Curves for Spaced Repetition Language Learning](https://mlsanity.com/paper/2004.11327/) (2020)
- [On the complexity of logistic regression models](https://mlsanity.com/paper/1903.00386/) (2019)

## Built on by (most cited first, 10 of 10)

- [Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA](https://mlsanity.com/paper/2305.14458/) (2023)
- [Personalized Jargon Identification for Enhanced Interdisciplinary Communication](https://mlsanity.com/paper/2311.09481/) (2023)
- [Japanese Lexical Complexity for Non-Native Readers: A New Dataset](https://mlsanity.com/paper/2306.17399/) (2023)
- [Personal Salience: Highlighting Is Social, but Individuality Lives in Selection](https://mlsanity.com/paper/2606.09024/) (2026)
- [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)
- [RALS: Resources and Baselines for Romanian Automatic Lexical Simplification](https://mlsanity.com/paper/2607.20078/) (2026)
- [Using Letter Positional Probabilities to Assess Word Complexity](https://mlsanity.com/paper/2404.07768/) (2024)
- [Explain Less, Understand More: Data-Efficient Personalization of Reader-Dependent Jargons](https://mlsanity.com/paper/2505.16227/) (2025)
- [Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings](https://mlsanity.com/paper/2508.10239/) (2025)

## Abstract (arXiv)

Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience. In this paper, we show that personal models are best when predicting word complexity for individual readers. We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research.