Computational linguists have long recognized the value of version control systems such as Git (and related platforms, e.g., GitHub) when it comes to managing and distributing computer code. However, the benefits of version control remain under-explored for a central activity within computational linguistics: the development of annotated natural language resources. We argue that researchers can employ version control practices to make development workflows more transparent, efficient, consistent, and participatory. We report a proof-of-concept, GitHub-based solution which facilitated the creation of a legal English treebank.

Legal interpretation frequently involves assessing how a legal text, as understood by an ‘ordinary’ speaker of the language, applies to the set of facts characterizing a legal dispute. Recent scholarship has proposed that legal practitioners add large language models (LLMs) to their interpretive toolkit. This work offers an empirical argument against LLM-assisted interpretation as recently practiced by legal scholars and federal judges. Our investigation in English shows that models do not provide stable interpretive judgments and are susceptible to subtle variations in the prompt. While instruction tuning slightly improves model calibration to human judgments, even the best-calibrated LLMs remain weak predictors of human native speakers’ judgments.

We introduce Legal-CGEL, an ongoing treebanking project focused on syntactic analysis of legal English text in the CGELBank framework (Reynolds et al., 2022), with an initial focus on US statutory law. When it comes to treebanking for legal English, we argue that there are unique advantages to employing CGELBank, a formalism that extends a comprehensive—and authoritative—formal description of English syntax (the Cambridge Grammar of the English Language; Huddleston & Pullum, 2002). We discuss some analytical challenges that have arisen in extending CGELBank to the legal domain. We conclude with a summary of immediate and longer-term project goals.

Our ability to limit the future spread of COVID-19 will in part depend on our understanding of the psychological and sociological processes that lead people to follow or reject coronavirus health behaviors. We argue that the virus has taken on heterogeneous meanings in communities across the United States and that these disparate meanings shaped communities’ response to the virus during the early, vital stages of the outbreak in the U.S. Using word embeddings, we demonstrate that counties where residents socially distanced less on average (as measured by residential mobility) more semantically associated the virus in their COVID discourse with concepts of fraud, the political left, and more benign illnesses like the flu. We also show that the different meanings the virus took on in different communities explains a substantial fraction of what we call the “”Trump Gap”, or the empirical tendency for more Trump-supporting counties to socially distance less. This work demonstrates that community-level processes of meaning-making in part determined behavioral responses to the COVID-19 pandemic and that these processes can be measured unobtrusively using Twitter.

Modern semantic analyses of epistemic language (incl. the modals must and might) can be characterized by the following ‘credence assumption’: speakers have full certainty regarding the propositions that structure their epistemic state. Intuitively, however: a) speakers have graded, rather than categorical, commitment to these propositions, which are often never fully and explicitly articulated; b) listeners have higher-order uncertainty about this speaker uncertainty; c) must p is used to communicate speaker commitment to some conclusion p and to indicate speaker commitment to the premises that condition the conclusion. I explore the consequences of relaxing the credence assumption by extending the argument system semantic framework first proposed by Stone (1994) to a Bayesian probabilistic framework of modeling pragmatic interpretation (Goodman and Frank, 2016). The analysis makes desirable predictions regarding the behavior and interpretation of must, and it suggests a new way of considering the nature of context and communicative exchange.