Stochastic model for the vocabulary growth in natural languages

We propose a stochastic model for the number of different words in a given database which incorporates the dependence on the database size and historical changes. The main feature of our model is the existence of two different classes of words: (i) a finite number of core-words which have higher frequency and do not affect the probability of a new word to be used; and (ii) the remaining virtually infinite number of noncore-words which have lower frequency and once used reduce the probability of a new word to be used in the future. Our model relies on a careful analysis of the google-ngram database of books published in the last centuries and its main consequence is the generalization of Zipf's and Heaps' law to two scaling regimes. We confirm that these generalizations yield the best simple description of the data among generic descriptive models and that the two free parameters depend only on the language but not on the database. From the point of view of our model the main change on historical time scales is the composition of the specific words included in the finite list of core-words, which we observe to decay exponentially in time with a rate of approximately 30 words per year for English.Comment: corrected typos and errors in reference list; 10 pages text, 15 pages supplemental material; to appear in Physical Review

Neutral evolution and turnover over centuries of English word popularity

Here we test Neutral models against the evolution of English word frequency and vocabulary at the population scale, as recorded in annual word frequencies from three centuries of English language books. Against these data, we test both static and dynamic predictions of two neutral models, including the relation between corpus size and vocabulary size, frequency distributions, and turnover within those frequency distributions. Although a commonly used Neutral model fails to replicate all these emergent properties at once, we find that modified two-stage Neutral model does replicate the static and dynamic properties of the corpus data. This two-stage model is meant to represent a relatively small corpus (population) of English books, analogous to a `canon', sampled by an exponentially increasing corpus of books in the wider population of authors. More broadly, this mode -- a smaller neutral model within a larger neutral model -- could represent more broadly those situations where mass attention is focused on a small subset of the cultural variants.Comment: 12 pages, 5 figures, 1 tabl

A fully data-driven method to identify (correlated) changes in diachronic corpora

In this paper, a method for measuring synchronic corpus (dis-)similarity put forward by Kilgarriff (2001) is adapted and extended to identify trends and correlated changes in diachronic text data, using the Corpus of Historical American English (Davies 2010a) and the Google Ngram Corpora (Michel et al. 2010a). This paper shows that this fully data-driven method, which extracts word types that have undergone the most pronounced change in frequency in a given period of time, is computationally very cheap and that it allows interpretations of diachronic trends that are both intuitively plausible and motivated from the perspective of information theory. Furthermore, it demonstrates that the method is able to identify correlated linguistic changes and diachronic shifts that can be linked to historical events. Finally, it can help to improve diachronic POS tagging and complement existing NLP approaches. This indicates that the approach can facilitate an improved understanding of diachronic processes in language change.Comment: typological changes only: reference-source-not-found-errors remove

Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution

It is tempting to treat frequency trends from the Google Books data sets as indicators of the true popularity of various words and phrases. Doing so allows us to draw quantitatively strong conclusions about the evolution of cultural perception of a given topic, such as time or gender. However, the Google Books corpus suffers from a number of limitations which make it an obscure mask of cultural popularity. A primary issue is that the corpus is in effect a library, containing one of each book. A single, prolific author is thereby able to noticeably insert new phrases into the Google Books lexicon, whether the author is widely read or not. With this understood, the Google Books corpus remains an important data set to be considered more lexicon-like than text-like. Here, we show that a distinct problematic feature arises from the inclusion of scientific texts, which have become an increasingly substantive portion of the corpus throughout the 1900s. The result is a surge of phrases typical to academic articles but less common in general, such as references to time in the form of citations. We use information theoretic methods to highlight these dynamics by examining and comparing major contributions via a divergence measure of English data sets between decades in the period 1800-2000. We find that only the English Fiction data set from the second version of the corpus is not heavily affected by professional texts. Overall, our findings call into question the vast majority of existing claims drawn from the Google Books corpus, and point to the need to fully characterize the dynamics of the corpus before using these data sets to draw broad conclusions about cultural and linguistic evolution

Anatomy of Scientific Evolution

The quest for historically impactful science and technology provides invaluable insight into the innovation dynamics of human society, yet many studies are limited to qualitative and small-scale approaches. Here, we investigate scientific evolution through systematic analysis of a massive corpus of digitized English texts between 1800 and 2008. Our analysis reveals great predictability for long-prevailing scientific concepts based on the levels of their prior usage. Interestingly, once a threshold of early adoption rates is passed even slightly, scientific concepts can exhibit sudden leaps in their eventual lifetimes. We developed a mechanistic model to account for such results, indicating that slowly-but-commonly adopted science and technology surprisingly tend to have higher innate strength than fast-and-commonly adopted ones. The model prediction for disciplines other than science was also well verified. Our approach sheds light on unbiased and quantitative analysis of scientific evolution in society, and may provide a useful basis for policy-making.Comment: Supplementary material attache

Measuring Linguistic and Cultural Evolution Using Books and Tweets

Written language provides a snapshot of linguistic, cultural, and current events information for a given time period. Aggregating these snapshots by studying many texts over time reveals trends in the evolution of language, culture, and society. The ever-increasing amount of electronic text, both from the digitization of books and other paper documents to the increasing frequency with which electronic text is used as a means of communication, has given us an unprecedented opportunity to study these trends. In this dissertation, we use hundreds of thousands of books spanning two centuries scanned by Google, and over 100 billion messages, or ‘tweets’, posted to the social media platform, Twitter, over the course of a decade to study the English language, as well as study the evolution of culture and society as inferred from the changes in language. We begin by studying the current state of verb regularization and how this compares between the more formal writing of books and the more colloquial writing of tweets on Twitter. We find that the extent of verb regularization is greater on Twitter, taken as a whole, than in English Fiction books, and also for tweets geotagged in the United States relative to American English books, but the opposite is true for tweets geotagged in the United Kingdom relative to British English books. We also find interesting regional variations in regularization across counties in the United States. However, once differences in population are accounted for, we do not identify strong correlations with socio-demographic variables. Next, we study stretchable words, a fundamental aspect of spoken language that, until the advent of social media, was rarely observed within written language. We examine the frequency distributions of stretchable words and introduce two central parameters that capture their main characteristics of balance and stretch. We explore their dynamics by creating visual tools we call ‘balance plots’ and ‘spelling trees’. We also discuss how the tools and methods we develop could be used to study mistypings and misspellings, and may have further applications both within and beyond language. Finally, we take a closer look at the English Fiction n-gram dataset created by Google. We begin by explaining why using token counts as a proxy of word, or more generally, ‘n-gram’, importance is fundamentally flawed. We then devise a method to rebuild the Google Books corpus so that meaningful linguistic and cultural trends may be reliably discerned. We use book counts as the primary ranking for an n-gram and use subsampling to normalize across time to mitigate the extraneous results created by the underlying exponential increase in data volume over time. We also combine the subsampled data over a number of years as a method of smoothing. We then use these improved methods to study linguistic and cultural evolution across the last two centuries. We examine the dynamics of Zipf distributions for n-grams by measuring the churn of language reflected in the flux of n-grams across rank boundaries. Finally, we examine linguistic change using wordshift plots and a rank divergence measure with a tunable parameter to compare the language of two different time periods. Our results address several methodological shortcomings associated with the raw Google Books data, strengthening the potential for cultural inference by word changes

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives

Computational approaches to semantic change

Semantic change — how the meanings of words change over time — has preoccupied scholars since well before modern linguistics emerged in the late 19th and early 20th century, ushering in a new methodological turn in the study of language change. Compared to changes in sound and grammar, semantic change is the least  understood. Ever since, the study of semantic change has progressed steadily, accumulating a vast store of knowledge for over a century, encompassing many languages and language families. Historical linguists also early on realized the potential of computers as research tools, with papers at the very first international conferences in computational linguistics in the 1960s. Such computational studies still tended to be small-scale, method-oriented, and qualitative. However, recent years have witnessed a sea-change in this regard. Big-data empirical quantitative investigations are now coming to the forefront, enabled by enormous advances in storage capability and processing power. Diachronic corpora have grown beyond imagination, defying exploration by traditional manual qualitative methods, and language technology has become increasingly data-driven and semantics-oriented. These developments present a golden opportunity for the empirical study of semantic change over both long and short time spans. A major challenge presently is to integrate the hard-earned  knowledge and expertise of traditional historical linguistics with  cutting-edge methodology explored primarily in computational linguistics. The idea for the present volume came out of a concrete response to this challenge.  The 1st International Workshop on Computational Approaches to Historical Language Change (LChange'19), at ACL 2019, brought together scholars from both fields. This volume offers a survey of this exciting new direction in the study of semantic change, a discussion of the many remaining challenges that we face in pursuing it, and considerably updated and extended versions of a selection of the contributions to the LChange'19 workshop, addressing both more theoretical problems —  e.g., discovery of "laws of semantic change" — and practical applications, such as information retrieval in longitudinal text archives