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Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals

Name: Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals
Creator: Shane Steinert-Threlkeld
Keywords: efficient communication, Computer science, Science, QC1-999, media_common.quotation_subject, General Physics and Astronomy, Astrophysics, simplicity, computer.software_genre, Article

descriptionPublicationkeyboard_double_arrow_right Article 14 Oct 2021MDPI AGEntropy, volume 23, page 1,335 (eissn: 1099-4300,

Authors: Shane Steinert-Threlkeld;

doi: 10.3390/e23101335

pmc: PMC8570335

pmid: 34682

Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals

- Summary
- Subjects
- References
  (69)
- Related research
  (1)
- Metrics

Abstract

While the languages of the world vary greatly, they exhibit systematic patterns, as well. Semantic universals are restrictions on the variation in meaning exhibit cross-linguistically (e.g., that, in all languages, expressions of a certain type can only denote meanings with a certain special property). This paper pursues an efficient communication analysis to explain the presence of semantic universals in a domain of function words: quantifiers. Two experiments measure how well languages do in optimally trading off between competing pressures of simplicity and informativeness. First, we show that artificial languages which more closely resemble natural languages are more optimal. Then, we introduce information-theoretic measures of degrees of semantic universals and show that these are not correlated with optimality in a random sample of artificial languages. These results suggest both that efficient communication shapes semantic typology in both content and function word domains, as well as that semantic universals may not stand in need of independent explanation.

Related Organizations

Washington State University
United States
University of Amsterdam
Netherlands
University of Mary
United States
University of Washington
United States

Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science media_common.quotation_subject computer.software_genre Meaning (philosophy of language) Simplicity Function (engineering) media_common business.industry Problem of universals Constructed language Variation (linguistics) Function word Artificial intelligence business computer Natural language Natural language processing

Keywords

efficient communication, Science, QC1-999, General Physics and Astronomy, Astrophysics, simplicity, Article, conservativity, semantic universals, quantifiers, Physics, Q, monotonicity, QB460-466, informativeness, semantic typology

69 references, page 1 of 7

1. Croft, W. Typology and Universals; Cambridge University Press: Cambridge, UK, 1990.

2. Hyman, L.M. Universals in phonology. Linguist. Rev. 2008, 25, 83-137. [CrossRef]

3. Von Fintel, K.; Matthewson, L. Universals in semantics. Linguist. Rev. 2008, 25, 139-201. [CrossRef] [OpenAIRE]

4. Newmeyer, F.J. Universals in syntax. Linguist. Rev. 2008, 25, 35-82. [CrossRef]

5. Ferrer-i Cancho, R. Euclidean distance between syntactically linked words. Phys. Rev. E 2004, 70, 056135. [CrossRef] [OpenAIRE]

6. Futrell, R.; Mahowald, K.; Gibson, E. Large-scale evidence of dependency length minimization in 37 languages. Proc. Natl. Acad. Sci. USA 2015, 112, 10336-10341. [CrossRef] [OpenAIRE]

7. Hahn, M.; Jurafsky, D.; Futrell, R. Universals of word order reflect optimization of grammars for efficient communication. Proc. Natl. Acad. Sci. USA 2020, 117, 2347-2353. [CrossRef] [OpenAIRE]

8. Zipf, G.K. Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology; Addison-Wesley Press: Boston, MA, USA, 1949.

9. Ferrer-i Cancho, R.; Sole, R.V. Least effort and the origins of scaling in human language. Proc. Natl. Acad. Sci. USA 2003, 100, 788-791. [CrossRef]

10. Piantadosi, S.T. Zipf's word frequency law in natural language: A critical review and future directions. Psychon. Bull. Rev. 2014, 21, 1112-1130. [CrossRef] [OpenAIRE]

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1 Research products, page 1 of 1

SimInf_Quantifiers software on GitHub
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Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	5
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	5
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average