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Objectives: With the legalization of cannabis in New Jersey on April 21, 2022, including the licensing of cannabis dispensaries, concerns have arisen about potential adverse events related to cannabis use. Here, we explore temporal trends and risk factors for cannabis-related harm in both adult and pediatric cannabis-related visits at a tertiary care academic institution. Methods: We performed a retrospective chart review and temporal trend analysis via the electronic health record from May 1, 2019 to October 31, 2022, covering 2 years before, and 6 months after, cannabis legalization in New Jersey. The pediatric charts identified were analyzed for root causes of adverse events, and changes in the frequency of specific unsafe practices since cannabis legalization were tracked. Results: We found that adult cannabis ED-related visits significantly increased during the COVID-19 pandemic and remained higher than pre-pandemic levels for the remainder of the study periods, without a significant change upon legalization. Pediatric rates of cannabis-related ED visits did not vary significantly during the study period. The vast majority of visits for children aged 0-12 years were related to accidental cannabis exposures-often a household member's edibles-whereas most visits for older children stemmed from intentional cannabis use. Conclusion: This project highlights the unintended consequences of wider cannabis access in New Jersey. Notably, cannabis use increased even before its legalization, presumably in response to the COVID-19 pandemic and its attendant mental health effects. Rates of cannabis use disorder and its highlight of other concurrent psychiatric disorders are important topics for both clinicians and lawmakers to consider.
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VerbNet-the most extensive online verb lexicon currently available for English-has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper.
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Natural language processing is the study of computer programs that can understand and produce human language. An important goal in the research to produce such technology is identifying the right meaning of words and phrases. In this paper, we give an overview of current research in three areas: (i) inducing word meaning; (ii) distinguishing different meanings of words used in context; and (iii) determining when the meaning of a phrase cannot straightforwardly be obtained from its parts. Manual construction of resources is labour intensive and costly and furthermore may not reflect the meanings that are useful for the task or data at hand. For this reason, we focus particularly on systems that use samples of language data to learn about meanings, rather than examples annotated by humans.