Universals versus historical contingencies in lexical evolution

Bochkarev, Vladimir V.; Solovyev, Valery D.; Wichmann, Søren

doi:10.1098/rsif.2014.0841

Cited by 45 publications

(41 citation statements)

References 25 publications

(40 reference statements)

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“…For example, several recent studies engage in establishing information-theoretic and corpus-based methods for linguistic typology, i.e., classifying and comparing languages according to their information encoding potential [10,[12][13][14][15][16], and how this potential evolves over time [17][18][19]. Similar methods have been applied to compare and distinguish non-linguistic sequences from written language [20,21], though it is controversial whether this helps with more fine-grained distinctions between symbolic systems and written language [22,23].…”

Section: Introductionmentioning

confidence: 99%

The Entropy of Words—Learnability and Expressivity across More than 1000 Languages

Bentz

Alikaniotis

Cysouw

et al. 2017

Entropy

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Abstract:The choice associated with words is a fundamental property of natural languages. It lies at the heart of quantitative linguistics, computational linguistics and language sciences more generally. Information theory gives us tools at hand to measure precisely the average amount of choice associated with words: the word entropy. Here, we use three parallel corpora, encompassing ca. 450 million words in 1916 texts and 1259 languages, to tackle some of the major conceptual and practical problems of word entropy estimation: dependence on text size, register, style and estimation method, as well as non-independence of words in co-text. We present two main findings: Firstly, word entropies display relatively narrow, unimodal distributions. There is no language in our sample with a unigram entropy of less than six bits/word. We argue that this is in line with information-theoretic models of communication. Languages are held in a narrow range by two fundamental pressures: word learnability and word expressivity, with a potential bias towards expressivity. Secondly, there is a strong linear relationship between unigram entropies and entropy rates. The entropy difference between words with and without co-textual information is narrowly distributed around ca. three bits/word. In other words, knowing the preceding text reduces the uncertainty of words by roughly the same amount across languages of the world.

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Section: Introductionmentioning

confidence: 99%

The Entropy of Words—Learnability and Expressivity across More than 1000 Languages

Bentz

Alikaniotis

Cysouw

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…The initial studies carried out on this large database have allowed scholars to address unprecedented questions about language usage. In particular, for the first time it was possible to study quantitatively aspects of cultural change as reflected in language (Michel et al, 2011;Greenfield, 2013), and rigorously assess overall vocabulary drift over the time span of two centuries (Bochkarev et al, 2014). Moreover, methods inspired in statistical mechanics of complex systems were used to study the dynamics of word birth and death (Petersen et al, 2012a), long-range fractal correlations in word frequencies over centuries (Gao et al, 2012), and the scaling behaviour of word frequencies over time as represented by Zipf's (1949) and Heaps' (1978) laws (Petersen et al, 2012b;Gerlach and Altmann, 2013).…”

Section: Introductionmentioning

confidence: 99%

Coherent oscillations in word-use data from 1700 to 2008

Montemurro

Zanette

2016

Palgrave Commun

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In written language, the choice of specific words is constrained by both grammatical requirements and the specific semantic context of the message to be transmitted. To a significant degree, the semantic context is in turn affected by a broad cultural and historical environment, which also influences matters of style and manners. Over time, those environmental factors leave an imprint in the statistics of language use, with some words becoming more common and other words being preferred less. Here we characterize the patterns of language use over time based on word statistics extracted from more than 4.5 million books written over a period of 308 years. We find evidence of novel systematic oscillatory patterns in word use with a consistent period narrowly distributed around 14 years. The specific phase relationships between different words show structure at two independent levels: first, there is a weak global phase modulation that is primarily linked to overall shifts in the vocabulary across time; and second, a stronger component dependent on well defined semantic relationships between words. In particular, complex network analysis reveals that semantically related words show strong phase coherence. Ultimately, these previously unknown patterns in the statistics of language may be a consequence of changes in the cultural framework that influences the thematic focus of writers.

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“…Word frequencies in large diachronic collections of texts (such as Google Books) are seen as an interesting way of observing and studying historical real-world changes (Bentley et al, 2014;Michel et al, 2011). It has also been noted that times of change and conflict, such as wars and revolutions, are observable in language dynamics, such as the emergence of new words (Bochkarev et al, 2014;Bochkarev et al, 2015) and word growth rates (Petersen et al, 2012). Petersen et al (2012) conclude that "[t]opical words in media can display long-term persistence patterns /.../ and can result in a new word having larger fitness than related 'out-of-date' words".…”

Section: Topical-cultural Impact On Corpora As An Assumptionmentioning

confidence: 99%

Quantifying the dynamics of topical fluctuations in language

et al. 2020

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Abstract The availability of large diachronic corpora has provided the impetus for a growing body of quantitative research on language evolution and meaning change. The central quantities in this research are token frequencies of linguistic elements in texts, with changes in frequency taken to reflect the popularity or selective fitness of an element. However, corpus frequencies may change for a wide variety of reasons, including purely random sampling effects, or because corpora are composed of contemporary media and fiction texts within which the underlying topics ebb and flow with cultural and socio-political trends. In this work, we introduce a simple model for controlling for topical fluctuations in corpora—the topical-cultural advection model—and demonstrate how it provides a robust baseline of variability in word frequency changes over time. We validate the model on a diachronic corpus spanning two centuries, and a carefully-controlled artificial language change scenario, and then use it to correct for topical fluctuations in historical time series. Finally, we use the model to show that the emergence of new words typically corresponds with the rise of a trending topic. This suggests that some lexical innovations occur due to growing communicative need in a subspace of the lexicon, and that the topical-cultural advection model can be used to quantify this.

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Universals versus historical contingencies in lexical evolution

Cited by 45 publications

References 25 publications

The Entropy of Words—Learnability and Expressivity across More than 1000 Languages

The Entropy of Words—Learnability and Expressivity across More than 1000 Languages

Coherent oscillations in word-use data from 1700 to 2008

Quantifying the dynamics of topical fluctuations in language

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