# Word frequency-rank relationship in tagged texts

arXiv: https://arxiv.org/abs/2102.10992 · HTML: https://mlsanity.com/paper/2102.10992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Language Model is All You Need: Natural Language Understanding as Question Answering](https://mlsanity.com/paper/2011.03023/) (2020)
- [Heaps' law and Heaps functions in tagged texts: Evidences of their linguistic relevance](https://mlsanity.com/paper/2001.02178/) (2020)
- [Supervised Seeded Iterated Learning for Interactive Language Learning](https://mlsanity.com/paper/2010.02975/) (2020)
- [Words ranking and Hirsch index for identifying the core of the hapaxes in political texts](https://mlsanity.com/paper/2006.07667/) (2020)
- [A joint text mining-rank size investigation of the rhetoric structures of the US Presidents' speeches](https://mlsanity.com/paper/1905.04705/) (2019)

## Abstract (arXiv)

We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes (nouns, verbs, and others) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role. Comparing with a null hypothesis which assumes that words belonging to each class are uniformly distributed across the frequency-ranked vocabulary of the whole work, we disclose statistically significant differences between the three classes. This results point to the fact that frequency-rank relationships may reflect linguistic features associated with grammatical function.