15 research outputs found
A Dynamical Systems Model for Language Change
Formalizing linguists' intuitions of language change as a dynamical system, we quantify the time course of language change including sudden vs. gradual changes in languages. We apply the computer model to the historical loss of Verb Second from Old French to modern French, showing that otherwise adequate grammatical theories can fail our new evolutionary criterion
Evaluating the role of quantitative modeling in language evolution
Models are a flourishing and indispensable area of research in language evolution. Here we highlight critical issues in using and interpreting models, and suggest viable approaches. First, contrasting models can explain the same data and similar modelling techniques can lead to diverging conclusions. This should act as a reminder to use the extreme malleability of modelling parsimoniously when interpreting results. Second, quantitative techniques similar to those used in modelling language evolution have proven themselves inadequate in other disciplines. Cross-disciplinary fertilization is crucial to avoid mistakes which have previously occurred in other areas. Finally, experimental validation is necessary both to sharpen models' hypotheses, and to support their conclusions. Our belief is that models should be interpreted as quantitative demonstrations of logical possibilities, rather than as direct sources of evidence. Only an integration of theoretical principles, quantitative proofs and empirical validation can allow research in the evolution of language to progress
Diffusion of Lexical Change in Social Media
Computer-mediated communication is driving fundamental changes in the nature
of written language. We investigate these changes by statistical analysis of a
dataset comprising 107 million Twitter messages (authored by 2.7 million unique
user accounts). Using a latent vector autoregressive model to aggregate across
thousands of words, we identify high-level patterns in diffusion of linguistic
change over the United States. Our model is robust to unpredictable changes in
Twitter's sampling rate, and provides a probabilistic characterization of the
relationship of macro-scale linguistic influence to a set of demographic and
geographic predictors. The results of this analysis offer support for prior
arguments that focus on geographical proximity and population size. However,
demographic similarity -- especially with regard to race -- plays an even more
central role, as cities with similar racial demographics are far more likely to
share linguistic influence. Rather than moving towards a single unified
"netspeak" dialect, language evolution in computer-mediated communication
reproduces existing fault lines in spoken American English.Comment: preprint of PLOS-ONE paper from November 2014; PLoS ONE 9(11) e11311
The Dynamical Principles of Storytelling
When considering the opening part of 1800 short stories, we find that the
first dozen paragraphs of the average narrative follow an action principle as
defined in arXiv:2309.06600. When the order of the paragraphs is shuffled, the
average no longer exhibits this property. The findings show that there is a
preferential direction we take in semantic space when starting a story,
possibly related to a common Western storytelling tradition as implied by
Aristotle in Poetics.Comment: 6 pages, 4 figures, 3 table
Narrative as a Dynamical System
There is increasing evidence that human activity in general, and narrative in
particular, can be treated as a dynamical system in the physics sense; a system
whose evolution is described by an action integral, such that the average of
all possible paths from point A to point B is given by the extremum of the
action. We create by construction three such paths by averaging about 500
different narratives, and we show that the average path is consistent with an
action principle.Comment: 9 pages, 9 figures, 1 tabl
How individuals change language
Languages emerge and change over time at the population level though interactions between individual speakers. It is, however, hard to directly observe how a single speaker's linguistic innovation precipitates a population-wide change in the language, and many theoretical proposals exist. We introduce a very general mathematical model that encompasses a wide variety of individual-level linguistic behaviours and provides statistical predictions for the population-level changes that result from them. This model allows us to compare the likelihood of empirically-attested changes in definite and indefinite articles in multiple languages under different assumptions on the way in which individuals learn and use language. We find that accounts of language change that appeal primarily to errors in childhood language acquisition are very weakly supported by the historical data, whereas those that allow speakers to change incrementally across the lifespan are more plausible, particularly when combined with social network effects
The Spread of Change in French Negation
Many varieties of French have changed over the years from expressing predicate negation (Geurts 1998) with ne alone, to the embracing construction ne ... pas, and then to postverbal pas alone (Jespersen 1917). When the increase in the frequency of ne ... pas over time is plotted on a graph, it takes the S shape of the logistic function (Kroch 1989). Bybee and Thompson (1997) note that the type frequency of a pattern determines its degree of productivity, but high frequency forms with alternations resist analogical leveling.\u27 These two observations provide an explanation for the logistic progression observed by Kroch (1989). Following Lotka (1925) and Volterra (1926), we can extend this model to take into account the competition between constructions to express the same function. To test these models, I have compiled a corpus of French theatrical texts from the twelfth to the twentieth century. The logistic function accurately models the use of ne ... pas in these texts (R2 = 0.899), but the Lotka-Volterra model predicts the post-1600 changes in preverbal ne alone and embracing ne ... pas and ne ... point with even greater accuracy (r = 0.948 and 0.978)