mlsanityData to 7 Oct 2026

Human Languages with Greater Information Density Increase Communication Speed, but Decrease Conversation Breadth

arXiv 2112.08491 ↗First posted 15 Dec 2021cs.CLcited by 5

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Abstract (arXiv)

Human languages vary widely in how they encode information within circumscribed semantic domains (e.g., time, space, color, human body parts and activities), but little is known about the global structure of semantic information and nothing about its relation to human communication. We first show that across a sample of ~1,000 languages, there is broad variation in how densely languages encode information into their words. Second, we show that this language information density is associated with a denser configuration of semantic information. Finally, we trace the relationship between language information density and patterns of communication, showing that informationally denser languages tend toward (1) faster communication, but (2) conceptually narrower conversations within which topics of conversation are discussed at greater depth. These results highlight an important source of variation across the human communicative channel, revealing that the structure of language shapes the nature and texture of human engagement, with consequences for human behavior across levels of society.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 11 of its 114 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

11 of 114 references.

References · 11

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  4. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  5. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  6. 2018Learning Word Vectors for 157 Languagescited by 279
  7. 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
  8. 2016Linear Algebraic Structure of Word Senses, with Applications to Polysemycited by 165
  9. 2018The Geometry of Culture: Analyzing Meaning through Word Embeddingscited by 87
  10. 2015On the universal structure of human lexical semanticscited by 8
  11. 2021Integrating topic modeling and word embedding to characterize violent deathscited by 8

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Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 2.

5 papers.