# Estimating Lexical Complexity from Document-Level Distributions

arXiv: https://arxiv.org/abs/2404.01196 · HTML: https://mlsanity.com/paper/2404.01196/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [SemEval-2021 Task 1: Lexical Complexity Prediction](https://mlsanity.com/paper/2106.00473/) (2021)

## Built on by (most cited first, 2 of 2)

- [Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training](https://mlsanity.com/paper/2512.21515/) (2025)
- [Towards Empowering Consumers through Sentence-level Readability Scoring in German ESG Reports](https://mlsanity.com/paper/2603.29861/) (2026)

## Abstract (arXiv)

Existing methods for complexity estimation are typically developed for entire documents. This limitation in scope makes them inapplicable for shorter pieces of text, such as health assessment tools. These typically consist of lists of independent sentences, all of which are too short for existing methods to apply. The choice of wording in these assessment tools is crucial, as both the cognitive capacity and the linguistic competency of the intended patient groups could vary substantially. As a first step towards creating better tools for supporting health practitioners, we develop a two-step approach for estimating lexical complexity that does not rely on any pre-annotated data. We implement our approach for the Norwegian language and verify its effectiveness using statistical testing and a qualitative evaluation of samples from real assessment tools. We also investigate the relationship between our complexity measure and certain features typically associated with complexity in the literature, such as word length, frequency, and the number of syllables.