Human behavior and the principle of least effort

By G. Zipf. 1949

Subfields: Cognitive Science & Psychology, Linguistics

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Citations in the corpus

WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (2018, background): “…for individual meanings of words, and 1 Ambiguous words are important as they constitute the most frequent words in a natural language (Zipf, 1949). contextualized word embeddings (Melamud et al., 2016; Peters et al., 2018), which instead compute a single dynamic embedding for a given…”
Shortcut learning in deep neural networks (2020, background): “In Linguistics, a related phenomenon is called the “Principle of Least Effort” [119], the observation that language speakers generally try to minimise the amount of effort involved in communication.”
Do Transformer Modifications Transfer Across Implementations and Applications? (2021, background): “Adaptive softmax (Joulin et al., 2017) uses the natural imbalance in word distributions (Zipf, 1949) to form clusters in a hierarchical model, which minimizes computation time.”
A Survey of Reinforcement Learning Informed by Natural Language (2019, background): “Real natural language has important statistical properties, such as the power-law distribution of word frequencies [Zipf, 1949] which does not appear in environments with synthetically generated language and small numbers of entities.”
Criticality in Formal Languages and Statistical Physics∗ (2017, background): “com/1099-4300/19/7/299 1 The power law discussed here should not be confused with another famous power law that occurs in natural languages: Zipf’s law [14].”
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, background): “For example, society is fractal because there are far more poor people than rich people, or far more ordinary people than extraordinary people (Zipf 1949).”
CENTRAL PLACE T HEORY A ND CITY SIZE DISTRIBUTION (2012, unknown): “If the tail index is exactly one, then the distribution is called Zipf’s law (following Zipf, 1949) or the rank-size rule, because the product of the rank and size of a city is a constant.”
The complex dynamics of collaborative tagging (2007, unknown): “[20].”
Data Distributional Properties Drive Emergent In-Context Learning in Transformers (2022, background): “Many natural phenomena such as word distributions take this form, and are classically described as a Zipﬁan (power law) distribution (Zipf, 1949): Here, X is the rank of the class (e.g. 1 for the most common class), and the exponent α ∈ [0 , ∞ ) determines the degree of skew. and Kucera, 1979).”
Beyond Gaussian averages: redirecting international business and management research toward extreme events and power laws (2007, background): “They are shown to apply to cities from 1790 to 1993 when ranked by population (Auerbach, 1913; Zipf, 1949; Krugman , 1996).”
Complexity and Empirical Economics (2005, background): “Zipf (1949) argued that many phenomena, ranging from the distributions”
Some Aspects of Optimality in Natural Language Interpretation (2000, methodology): “With Zipf (1949) as a forerunner we have to acknowledge two basic and competing forces, one force of unification, or Speaker's economy, and the antithetical force of diversification, or Auditor's economy.”