Extracting information from S-curves of language change

It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period, and slow end). In this paper, we analyze how much information on the dynamics of innovation spreading can be obtained from a quantitative description of S-curves. We focus on the adoption of linguistic innovations for which detailed databases of written texts from the last 200 years allow for an unprecedented statistical precision. Combining data analysis with simulations of simple models (e.g., the Bass dynamics on complex networks) we identify signatures of endogenous and exogenous factors in the S-curves of adoption. We propose a measure to quantify the strength of these factors and three different methods to estimate it from S-curves. We obtain cases in which the exogenous factors are dominant (in the adoption of German orthographic reforms and of one irregular verb) and cases in which endogenous factors are dominant (in the adoption of conventions for romanization of Russian names and in the regularization of most studied verbs). These results show that the shape of S-curve is not universal and contains information on the adoption mechanism. (published at "J. R. Soc. Interface, vol. 11, no. 101, (2014) 1044"; DOI: http://dx.doi.org/10.1098/rsif.2014.1044)Comment: 9 pages, 5 figures, Supplementary Material is available at http://dx.doi.org/10.6084/m9.figshare.122178

The Natural Selection of Words: Finding the Features of Fitness

We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of 4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to 2000 AD. We present a supervised learning algorithm that is able to predict the future leader of a synset: the word in the synset that will have the highest frequency. The algorithm uses features based on a word's length, the characters in the word, and the historical frequencies of the word. It can predict change of leadership (including the identity of the new leader) fifty years in the future, with an F-score considerably above random guessing. Analysis of the learned models provides insight into the causes of change in the leader of a synset. The algorithm confirms observations linguists have made, such as the trend to replace the -ise suffix with -ize, the rivalry between the -ity and -ness suffixes, and the struggle between economy (shorter words are easier to remember and to write) and clarity (longer words are more distinctive and less likely to be confused with one another). The results indicate that integration of the Google Books Ngram Corpus with WordNet has significant potential for improving our understanding of how language evolves

Challenges in detecting evolutionary forces in language change using diachronic corpora

Newberry et al. (Detecting evolutionary forces in language change, 'Nature' 551, 2017) tackle an important but difficult problem in linguistics, the testing of selective theories of language change against a null model of drift. Having applied a test from population genetics (the Frequency Increment Test) to a number of relevant examples, they suggest stochasticity has a previously under-appreciated role in language evolution. We replicate their results and find that while the overall observation holds, results produced by this approach on individual time series can be sensitive to how the corpus is organized into temporal segments (binning). Furthermore, we use a large set of simulations in conjunction with binning to systematically explore the range of applicability of the Frequency Increment Test. We conclude that care should be exercised with interpreting results of tests like the Frequency Increment Test on individual series, given the researcher degrees of freedom available when applying the test to corpus data, and fundamental differences between genetic and linguistic data. Our findings have implications for selection testing and temporal binning in general, as well as demonstrating the usefulness of simulations for evaluating methods newly introduced to the field

From conditionality to modality in Luganda (Bantu, JE15) : a synchronic and diachronic corpus analysis of the verbal prefix -andi-

This article offers a synchronic and diachronic analysis of the use and meaning of the verbal prefix-andi-in the Great Lakes Bantu language Luganda (JE15). On the basis of a text corpus of 4 million tokens, we show that the prefix, commonly described as a conditional marker, is primarily involved in the expression of modal meanings, specifically deontic necessity and epistemic possibility. Our thirteen-decade diachronic corpus analysis shows that there is a relationship between the increased use of-andi-outside syntactically complex conditional constructions, i.e. those having both a protasis and an apodosis, and an increase in its expression of modal meanings. Moreover, a reduction in the use of-andiin complex conditional constructions goes hand in hand with a reduction in its expression of conditional meanings. It is further revealed that contrary to the common cross-linguistic tendency to rely on modality as a source for conditionality, the conditional meaning of-andi-is not post-modal. Instead it was primarily a conditional marker, which subsequently developed different modal meanings. (C) 2018 Elsevier B.V. All rights reserved

Quantifying the dynamics of topical fluctuations in language

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.Comment: Code to run the analyses described in this paper is now available at https://github.com/andreskarjus/topical_cultural_advection_model . A previous shorter version of this paper outlining the basic model appeared as an extended abstract in the proceedings of the Society for Computation in Linguistics (Karjus et al. 2018, Topical advection as a baseline model for corpus-based lexical dynamics