Issues in the creation of a digital archive of a signed language

PARADISEC (Pacific And Regional Archive for Digital Sources in Endangered Cultures), Australian Partnership for Sustainable Repositories, Ethnographic E-Research Project and Sydney Object Repositories for Research and Teaching

Indicating verbs as typologically unique constructions: Reconsidering verb ‘agreement’ in sign languages

In this paper, we present arguments for an analysis of indicating verbs, building on Liddell (2000), as a typologically unique, unimodal fusion of signs and pointing gestures used for reference tracking. This contrasts with many formalist analyses that assume that directionality in indicating verbs constitutes an agreement marking system. While exploring some of the debate in the literature about these forms, we propose a model of indicating verbs within a Construction Grammar framework that compares them to multimodal constructions in spoken languages. We explain how our model of indicating verbs appear to align with a growing body of research on co-speech gesture and is supported by some recent findings about these verbs from corpus-based studies of sign languages

Graph traverse reference network for sign language corpus retrieval in the wild

Sign languages are the primary languages of the deaf community as well as hearing individuals who are unable to speak, which engage the visual-manual modality to convey meanings. In recent years, there has been an explosive growth of sign language videos available from video streaming and social media service platforms. Given the size of these corpora, sign language users often face significant challenges in effectively acquiring the information they need. Therefore, we propose a novel deep learning architecture, namely Graph Traverse Reference Network (GTRN), allowing visual signing queries to retrieve relevant sign language videos (documents) from a large corpus. GTRN introduces a traverse graph, which provides coarse-to-fine reference information in a hierarchical manner from frame-level to body-part-level observations. A reference-based attention is devised to obtain the embedding for a visual input of each level, which allows the computations to be allocated and processed at difference locations regarding local devices and central servers. A contrastive learning strategy optimizes GTRN in pursuit of a joint latent space for the queries and the documents by their meanings. Moreover, GTRN is compatible to leverage existing general visual representation foundation models, by which their resulted embeddings are used as the frame-level reference of GTRN. To the best of our knowledge, it is one of the first studies on using visual signing queries for retrieving sign language videos in a real-world setting and comprehensive experiments were conducted which demonstrated the effectiveness of our proposed method

The social structure of signing communities and lexical variation:A cross-linguistic comparison of three unrelated sign languages

Claims have been made about the relationship between the degree of lexical variation and the social structure of a sign language community (e.g., population size), but to date there exist no large-scale cross-linguistic comparisons to address these claims. In this study, we present a cross-linguistic analysis of lexical variation in three signing communities: Kata Kolok, Israeli Sign Language (ISL) and British Sign Language (BSL). Contrary to the prediction that BSL would have the lowest degree of lexical variation because it has the largest population size, we found that BSL has the highest degree of lexical variation across the entire community (i.e., at the global level). We find, however, that BSL has the lowest degree of lexical variation at the local level, i.e., within clusters of participants who group most similarly lexically. Kata Kolok and ISL, on the other hand, exhibit less of a distinction between variation at the global and local levels, suggesting that lexical variation does not pattern as strongly within subsets of these two communities as does BSL. The results of this study require us to reassess claims made about lexical variation and community structure; we need to move towards an approach of studying (lexical) variation which treats communities equally on a theoretical level and which respects the unique social-demographic profile of each community when designing the analysis by using a community-centered approach.</p

The BRICS (Bronchiectasis Radiologically Indexed CT Score)- a multi-center study score for use in idiopathic and post infective bronchiectasis

OBJECTIVES: The goal of this study was to develop a simplified radiological score that could assess clinical disease severity in bronchiectasis. METHODS: The Bronchiectasis Radiologically Indexed CT Score (BRICS) was devised based on a multivariable analysis of the Bhalla score and its ability in predicting clinical parameters of severity. The score was then externally validated in six centers in 302 patients. RESULTS: A total of 184 high-resolution CT scans were scored for the validation cohort. In a multiple logistic regression model, disease severity markers significantly associated with the Bhalla score were percent predicted FEV1, sputum purulence, and exacerbations requiring hospital admission. Components of the Bhalla score that were significantly associated with the disease severity markers were bronchial dilatation and number of bronchopulmonary segments with emphysema. The BRICS was developed with these two parameters. The receiver operating-characteristic curve values for BRICS in the derivation cohort were 0.79 for percent predicted FEV1, 0.71 for sputum purulence, and 0.75 for hospital admissions per year; these values were 0.81, 0.70, and 0.70, respectively, in the validation cohort. Sputum free neutrophil elastase activity was significantly elevated in the group with emphysema on CT imaging. CONCLUSIONS: A simplified CT scoring system can be used as an adjunct to clinical parameters to predict disease severity in patients with idiopathic and postinfective bronchiectasis

The learnability of sign language morphology: an experimental study

Linguistic features are adapted to their sociolinguistic ecologies (Lupyan &amp; Dale, 2010). In this way, we posit that the morphological features of sign languages have evolved to be learnable and iconic, as many sign languages have a large proportion of second language learners and delayed first language learners. To test the learnability of sign language morphology, we conceptually replicate Smith (2024), teaching participants (hearing non-signers) British Sign Language (BSL) descriptions (lexical item plus classifier construction depicting the referent and movement) of scenes where animals perform movements. We test how accurately and how quickly participants perform in two conditions: a BSL condition pairing scenes with the BSL descriptions and a counter-iconic condition randomly pairing BSL descriptions to scenes. Following our pre-registered analysis, our pilot study suggests that BSL condition participants perform faster and more accurately than those in the counter-iconic condition. Data collection for our online learning study is underway

Sociolinguistic typology and sign languages

This paper examines the possible relationship between proposed social determinants of morphological ‘complexity’ and how this contributes to linguistic diversity, specifically via the typological nature of the sign languages of deaf communities. We sketch how the notion of morphological complexity, as defined by Trudgill (2011), applies to sign languages. Using these criteria, sign languages appear to be languages with low to moderate levels of morphological complexity. This may partly reflect the influence of key social characteristics of communities on the typological nature of languages. Although many deaf communities are relatively small and may involve dense social networks (both social characteristics that Trudgill claimed may lend themselves to morphological ‘complexification’), the picture is complicated by the highly variable nature of the sign language acquisition for most deaf people, and the ongoing contact between native signers, hearing non-native signers, and those deaf individuals who only acquire sign languages in later childhood and early adulthood. These are all factors that may work against the emergence of morphological complexification. The relationship between linguistic typology and these key social factors may lead to a better understanding of the nature of sign language grammar. This perspective stands in contrast to other work where sign languages are sometimes presented as having complex morphology despite being young languages (e.g., Aronoff et al., 2005); in some descriptions, the social determinants of morphological complexity have not received much attention, nor has the notion of complexity itself been specifically explored