Word frequency-rank relationship in tagged texts
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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.
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References · 5
- 2020Language Model is All You Need: Natural Language Understanding as Question Answeringcited by 29
- 2020Heaps' law and Heaps functions in tagged texts: Evidences of their linguistic relevancecited by 8
- 2020Supervised Seeded Iterated Learning for Interactive Language Learningcited by 7
- 2020Words ranking and Hirsch index for identifying the core of the hapaxes in political textscited by 2
- 2019A joint text mining-rank size investigation of the rhetoric structures of the US Presidents' speechescited by 1
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