We investigate the descriptive relevance of widely used semantic change models in linguistic descriptions of present-day speech communities.We focus on the sociolinguistic issue of contact-induced semantic shifts in Quebec English, and analyze 40 target words using type-level and token-level word embeddings, empirical linguistic properties, and -cruciallyacceptability ratings and qualitative remarks by 15 speakers from Montreal.Our results confirm the overall relevance of the computational approaches, but also highlight practical issues and the complementary nature of different semantic change estimates.To our knowledge, this is the first study to substantively engage with the speech community being described using semantic change models.

This study investigates the applicability of semantic change detection methods in descriptively oriented linguistic research.It specifically focuses on contact-induced semantic shifts in Quebec English.We contrast synchronic data from different regions in order to identify the meanings that are specific to Quebec and potentially related to language contact.Type-level embeddings are used to detect new semantic shifts, and token-level embeddings to isolate regionally specific occurrences.We introduce a new 80-item test set and conduct both quantitative and qualitative evaluations.We demonstrate that diachronic word embedding methods can be applied to contactinduced semantic shifts observed in synchrony, obtaining results comparable to the state of the art on similar tasks in diachrony.However, we show that encouraging evaluation results do not translate to practical value in detecting new semantic shifts.Finally, our application of token-level embeddings accelerates manual data exploration and provides an efficient way of scaling up sociolinguistic analyses.

We present a 78.8-million-tweet, 1.3-billion-word corpus aimed at studying regional variation in Canadian English with a specific focus on the dialect regions of Toronto, Montreal, and Vancouver. Our data collection and filtering pipeline reflects complex design criteria, which aim to allow for both data-intensive modeling methods and user-level variationist sociolinguistic analysis. It specifically consists in identifying Twitter users from the three cities, crawling their entire timelines, filtering the collected data in terms of user location and tweet language, and automatically excluding near-duplicate content. The resulting corpus mirrors national and regional specificities of Canadian English, it provides sufficient aggregate and user-level data, and it maintains a reasonably balanced distribution of content across regions and users. The utility of this dataset is illustrated by two example applications: the detection of regional lexical and topical variation, and the identification of contact-induced semantic shifts using vector space models. In accordance with Twitter’s developer policy, the corpus will be publicly released in the form of tweet IDs.

We present a study in which we compare 11 different French dependency parsers on a specialized corpus (consisting of research articles on NLP from the proceedings of the TALN conference). Due to the lack of a suitable gold standard, we use each of the parsers’ output to generate distributional thesauri using a frequency-based method. We compare these 11 thesauri to assess the impact of choosing a parser over another. We show that, without any reference data, we can still identify relevant subsets among the different parsers. We also show that the similarity we identify between parsers is confirmed on a restricted distributional benchmark.

This article presents an investigation of the role of social relations in the writing of scientific articles through the study of in‐text citations. Does the fact that the author of an article knows the author whose work he or she cites have an impact on the context of the citation? Because citations are commonly used as criteria for research evaluation, it is important to question their social background to better understand how it impacts textual features. We studied a collection of science articles (N = 123) from 5 disciplines and interviewed their authors (N = 84) to: (a) identify the social relations between citing and cited authors; and (b) measure the correlation between a set of features related to in‐text citations (N = 6,956) and the identified social relations. Our pioneering work, mixing sociological and linguistic results, shows that social relations between authors can partly explain the variations of citations in terms of frequency, position and textual context.