Carl Börstell


2023

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Ableist Language Teching over Sign Language Research
Carl Börstell
Proceedings of the Second Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2023)

The progress made in computer-assisted linguistics has led to huge advances in natural language processing (NLP) research. This research often benefits linguistics in a broader sense, e.g., by digitizing pre-existing data and analyzing ever larger quantities of linguistic data in audio or visual form, such as sign language video data using computer vision methods. A large portion of research conducted on sign languages today is based in computer science and engineering, but much of this research is unfortunately conducted without any input from experts on the linguistics of sign languages or deaf communities. This is obvious from some of the language used in the published research, which regularly contains ableist labels. In this paper, I illustrate this by demonstrating the distribution of words in titles of research papers indexed by Google Scholar. By doing so, we see that the number of tech papers is increasing while the number of linguistics papers is (relatively) decreasing, and that ableist language is more frequent in tech papers. By extension, this suggest that much of the tech-related work on sign languages – heavily under-researched and under-resourced languages – is conducted without collaboration and consultation with deaf communities and experts, against ethical recommendations.

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Extracting Sign Language Articulation from Videos with MediaPipe
Carl Börstell
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

This paper concerns evaluating methods for extracting phonological information of Swedish Sign Language signs from video data with MediaPipe’s pose estimation. The methods involve estimating i) the articulation phase, ii) hand dominance (left vs. right), iii) the number of hands articulating (one- vs. two-handed signs) and iv) the sign’s place of articulation. The results show that MediaPipe’s tracking of the hands’ location and movement in videos can be used to estimate the articulation phase of signs. Whereas the inclusion of transport movements improves the accuracy for the estimation of hand dominance and number of hands, removing transport movements is crucial for estimating a sign’s place of articulation.

2022

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Introducing the signglossR Package
Carl Börstell
Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources

The signglossR package is a library written in the programming language R, intended as an easy-to-use resource for those who work with signed language data and are familiar with R. The package contains a variety of functions designed specifically towards signed language research, facilitating a single-pipeline workflow with R when accessing public language resources remotely (online) or a user’s own files and data. The package specifically targets processing of image and video files, but also features some interaction with software commonly used by researchers working on signed language and gesture, such as ELAN and OpenPose. The signglossR package combines features and functionality from many other libraries and tools in order to simplify and collect existing resources in one place, as well as adding some new functionality, and adapt everything to the needs of researchers working with visual language data. In this paper, the main features of this package are introduced.

2020

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Measuring Lexical Similarity across Sign Languages in Global Signbank
Carl Börstell | Onno Crasborn | Lori Whynot
Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives

Lexicostatistics is the main method used in previous work measuring linguistic distances between sign languages. As a method, it disregards any possible structural/grammatical similarity, instead focusing exclusively on lexical items, but it is time consuming as it requires some comparable phonological coding (i.e. form description) as well as concept matching (i.e. meaning description) of signs across the sign languages to be compared. In this paper, we present a novel approach for measuring lexical similarity across any two sign languages using the Global Signbank platform, a lexical database of uniformly coded signs. The method involves a feature-by-feature comparison of all matched phonological features. This method can be used in two distinct ways: 1) automatically comparing the amount of lexical overlap between two sign languages (with a more detailed feature-description than previous lexicostatistical methods); 2) finding exact form-matches across languages that are either matched or mismatched in meaning (i.e. true or false friends). We show the feasability of this method by comparing three languages (datasets) in Global Signbank, and are currently expanding both the size of these three as well as the total number of datasets.

2017

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Iconic Locations in Swedish Sign Language: Mapping Form to Meaning with Lexical Databases
Carl Börstell | Robert Östling
Proceedings of the 21st Nordic Conference on Computational Linguistics

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Universal Dependencies for Swedish Sign Language
Robert Östling | Carl Börstell | Moa Gärdenfors | Mats Wirén
Proceedings of the 21st Nordic Conference on Computational Linguistics

2016

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Morphological Complexity Influences Verb-Object Order in Swedish Sign Language
Johannes Bjerva | Carl Börstell
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)

Computational linguistic approaches to sign languages could benefit from investigating how complexity influences structure. We investigate whether morphological complexity has an effect on the order of Verb (V) and Object (O) in Swedish Sign Language (SSL), on the basis of elicited data from five Deaf signers. We find a significant difference in the distribution of the orderings OV vs. VO, based on an analysis of morphological weight. While morphologically heavy verbs exhibit a general preference for OV, humanness seems to affect the ordering in the opposite direction, with [+human] Objects pushing towards a preference for VO.

2015

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Enriching the Swedish Sign Language Corpus with Part of Speech Tags Using Joint Bayesian Word Alignment and Annotation Transfer
Robert Östling | Carl Börstell | Lars Wallin
Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)