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Interlinear-glossed text (IGT) is a method of representing semantic, morphological and phonological information about lexemes along with phrase and clause level translations of connected text. While the Leipzig Glossing Rules (LGR) provide general standards and principles for IGT, we argue here that language-family specific guidelines are necessary to facilitate rapid creation of new interpretable IGT that can be used for language description, typological discovery, and cross-language comparison. Using selected examples of Tibeto-Burman IGTs, we demonstrate how linguists create their own terminology and conventions for representing linguistic phenomena which fall outside the scope of the LGR. To date, there are few, at least within the Sino-Tibetan linguistics community, that have discussed language-family specific IGT conventions, so new annotators lack guidance on IGT creation. This paper examines how typical Tibeto-Burman constructions (e.g., reduplication, verb stem alternation, directionals) are represented in IGT from several South Central Tibeto-Burman languages. We offer some remarks on the purposes of IGT and some principles for new IGT creators. 

There is a growing interest in applying computational tools to the automatic discovery of social and economic behavior. For example, with decisions involving resource allocation related to public infrastructure, the ability to predict failures can allow for more efficient policy responses. In this paper, we use social data from a popular electric vehicle (EV) driver app to characterize the emerging EV charging station infrastructure. We introduce a typology of EV charging experiences collected from user reviews and deploy text classification algorithms, including convolutional neural networks (CNN), to automatically learn about potential failures. We use machine learning techniques as a pre-processing tool for econometric analyses on the quality of service delivery. After classifying the reviews into 9 main user topics and 34 subtopics, we find that the dominant issues in EV charging relate to station functionality and availability, which drive negative consumer experience. Contrary to the public discourse about EVs, range anxiety was not of large concern to existing EV drivers. Based on our findings, we move towards automated identification of failures in public charging infrastructure that can significantly reduce research evaluation costs through relatively simple computational solutions.