The Psycho-Biology Of Language: AN INTRODUCTION TO DYNAMIC PHILOLOGY

By G. Zipf. 1999

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LVIS: A Dataset for Large Vocabulary Instance Segmentation (2019, background): “umber of categories and per-category data is sometimes scarce. The long tail of rare categories is inescapable; annotating more images simply uncovers previously unseen, rare categories (see Fig.9and [38,33,31,35]). Efﬁciently learning from few examples is a signiﬁcant open problem in machine learni”
The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation (2021, background): “A comparison of differences in performance between Global and Random test sets provides insights into how sparsity impacts generalization performance on text which is common relative to a more typical Zipf distribution with long-tail features (Zipf, 1999).”
Heritage language education : a new field emerging (2008, background): “While the correlation between phonetic weight (word length) and frequency is well established ( Zipf, 1935; Bybee, 2001), even the small sample of verbs in Table 5 does not uphold this generalization; compare sidet’ vs. sest’ ‘sit’ where the longer word wins out, or the fairly equal in length pryga”
Incidental vocabulary learning in a natural reading context: an eye-tracking study (2017, background): “In following this two-pronged approach, we provide a more robust answer to how repeated exposure affects reading times, while working within the constraints of a natural language, which, by nature, has a skewed distribution (Zipf, 1935).”
Informativity affects consonant duration and deletion rates (2015, unknown): “Segment frequency follows similar patterns cross-linguistically but is not identical across languages (Zipf 1935: part III; Cohen Priva 2012: Ch. 5), and the exact distribution of segment frequencies is expected to be unique to English.”
A Brief History of Generative Models for Power Law and Lognormal Distributions (2004, background): “Similarly, Zipf is often redited with noting that ity sizes appear to mat h a power law, although this idea an be tra ed ba k further to 1913 and Auerba h [5℄.”
Class-Weighted Classification: Trade-offs and Robust Approaches (2020, background): “In text data, the distribution of words in documents has been observed to follow a power law in that there are many labels with few instances (Zipf, 1936; Feldman, 2019).”
The acquisition of tense-aspect: Converging evidence from corpora and telicity ratings (2009, background): “…be made on the basis of the corpus data is that the verb frequency distributions across the different TA categories can be described by Zipf's law (Zipf, 1935): The frequency with which verbs occur with a certain tense–aspect category is inversely proportional to its rank in the frequency table;…”
Computing the meanings of words in reading: cooperative division of labor between visual and phonological processes. (2004, background): “However, Zipf’s law (Zipf, 1935) states that there is a constant relationship between the number of words at a given frequency range and the square of that frequency range; i.”
Operant variability and voluntary action. (2010, background): “This equation closely resembles Zipf’s law, first observed in philology (Zipf, 1935); it describes the distribution of word frequencies in a variety of contexts.”
Exploring Balanced Feature Spaces for Representation Learning (2021, background): “In practice, the visual data usually follow a long-tailed distribution (Zipf, 1999; Spain & Perona, 2007), challenging supervised learning methods.”
A review of theory and practice in scientometrics (2015, background): “n = k nN 0 where k = Bradford coefficient, N 0 th= number in core zone, N n = journals in the n zone; Thus the number of journals needed to publish the same number of articles grows with a power law. Zipf (1936) studied the frequency of words in a text and postulated that the rank of the frequency o”