mlsanityData to 14 Oct 2026

Entropy and type-token ratio in gigaword corpora

arXiv 2411.10227 ↗First posted 15 Nov 2024cs.CL cs.IR physics.soc-phcited by 6

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Abstract (arXiv)

There are different ways of measuring diversity in complex systems. In particular, in language, lexical diversity is characterized in terms of the type-token ratio and the word entropy. We here investigate both diversity metrics in six massive linguistic datasets in English, Spanish, and Turkish, consisting of books, news articles, and tweets. These gigaword corpora correspond to languages with distinct morphological features and differ in registers and genres, thus constituting a varied testbed for a quantitative approach to lexical diversity. We unveil an empirical functional relation between entropy and type-token ratio of texts of a given corpus and language, which is a consequence of the statistical laws observed in natural language. Further, in the limit of large text lengths we find an analytical expression for this relation relying on both Zipf and Heaps laws that agrees with our empirical findings.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 19 of its 101 references that are in the map. First posted on arXiv 15 Nov 2024.

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19 of 101 references.

References · 19

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7 more
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Citing papers per year: 2025: 2; 2026: 4.

6 papers.