Measuring Lexical Diversity in Texts: The Twofold Length Problem
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Abstract (arXiv)
The impact of text length on the estimation of lexical diversity has captured the attention of the scientific community for more than a century. Numerous indices have been proposed, and many studies have been conducted to evaluate them, but the problem remains. This methodological review provides a critical analysis not only of the most commonly used indices in language learning studies, but also of the length problem itself, as well as of the methodology for evaluating the proposed solutions. The analysis of three datasets of English language-learners' texts revealed that indices that reduce all texts to the same length using a probabilistic or an algorithmic approach solve the length dependency problem; however, all these indices failed to address the second problem, which is their sensitivity to the parameter that determines the length to which the texts are reduced. The paper concludes with recommendations for optimizing lexical diversity analysis.
Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 0 of its 52 references that are in the map. First posted on arXiv 10 Jul 2023.
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Citing papers per year: 2024: 2; 2025: 4; 2026: 2.
8 papers.
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- 2026The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?12 Jan 2026
- 2025TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning3 Oct 2025
- 2025A survey of diversity quantification in natural language processing: The why, what, where and how28 Jul 2025
- 2025Mass-Scale Analysis of In-the-Wild Conversations Reveals Complexity Bounds on LLM Jailbreaking6 Jul 2025
- 2025Unstructured Evidence Attribution for Long Context Query Focused Summarization20 Feb 2025
- 2024Benchmarking Linguistic Diversity of Large Language Models13 Dec 2024
- 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models4 Aug 2024