PURPOSE: The purpose of the current study was to create a set of video-recorded real-sign-pseudosign pairs for Swedish Sign Language (Svenskt teckenspråk, STS). The pseudosigns should be based on the overall phonotactic structures of STS; each real-sign-pseudosign pair should be matched in terms of neighborhood density (ND) and phonotactic probability (PP); the real signs and pseudosigns should have similar distributions of ND, PP, and duration; and the set as a whole should have distributions of ND, PP, and (for the real signs) sign frequency (SF) similar to that of the STS. To achieve this, a secondary purpose was to develop algorithms to calculate ND and PP for STS and to investigate how the metrics correlate. METHOD: Based on publicly available data sources for STS, an initial data set was formed, which was utilized in an automatic algorithm to generate phonotactically feasible pseudosign candidates, as well as to calculate ND and PP values for both the real signs and the generated pseudosign candidates. The use of an automatic matching algorithm was followed by manual evaluation of the selected pseudosign candidates. The selected matching pairs of signs and pseudosigns were video recorded by four actors (two males and two females). RESULTS: Four hundred sixty-six sign-pseudosign pairs, matching in ND and PP, were video-recorded. The real signs and pseudosigns had similar distributions of ND, PP, and duration, and the distributions of ND, PP, and SF values in the recorded set were similar to those of the STS as a whole. CONCLUSIONS: The resulting data set has been made publicly available and is suitable for use in psycholinguistic experiments involving STS. The present work presents a new standard for estimation of lexical metrics in sign language that can be applied to other sign languages than STS in future studies. 

The purpose of the interlingual index is to link the lexical resources of the sign languages of the project. This is the final project release of the index, which covers both the core sign languages of the project as well as additional sign languages.

The Multilingual Sign Language Wordnet is the first publicly available wordnet resource for sign languages. It is a growing multilingual resource providing data for eight sign languages to date. During the initial phase of its creation, the focus lay on producing the infrastructure to support various languages and to produce initial sets of content for them. This article represents the start of the second phase, in which the focus is moved to establishing overlapping coverage across the different sign languages. Building on the data produced so far, a new feature to assist annotation is introduced which leverages established partial synonymy between signs (inter- and cross-lingually) to discover likely additional synonymies. Other improvements to the annotation interface and workflow build directly on the experiences from the first phase. Working with the updated annotation interface, new data is produced for Polish Sign Language, Greek Sign Language and Swedish Sign Language.

The Swedish Sign Language Dictionary [Svenskt teckenspråkslexikon] is one of the most visited websites at Stockholm University, with four million visits each year. The dictionary is an easy-to-use resource for the community, families, relatives, students, educators, researchers and other stakeholders that can be accessed through the website, app, and mobile platforms. STS-korpus is an online interface for the Swedish Sign Language Corpus that is linked to the STS Dictionary, enhancing its utility. Other applications, like TSP Quiz and the STS transcription tool, will also be evaluated. In January 2024, we conducted a survey to explore how users utilise Swedish sign language resources in their everyday lives, studies and work, regardless of hearing status and sign language skills. The purpose is to evaluate these resources from a user’s perspective, including aspects such as user-friendliness, relevance, comprehensibility and effectiveness in aiding language learning or communication.

Swedish Sign Language Dictionary, first published in 2008, was updated to a new version in May 2022. This version has improved search functions and user interface. The lexical database has over 24,000 unique signs and 6,600 example sentences and is being continuously updated with new signs and examples. Each entry contains rich information in the form of video and photos demonstrating the sign, as well as in text and sign transcription describing the execution of the sign. For many signs, there is additional information, for instance, on the use of the sign in context or on the sign’s origin. The different search paths that have been developed are based on the structure of the signs and on different subject areas. Swedish Sign Language Dictionary is connected to the Swedish Sign Language Corpus, which currently contains approximately 190,000 occurrences of signs. This collaboration takes place via the STS-korpus, a web-based tool for the  presentation of signs in natural language use. The collaboration between these two language resources is also concerned with lexicographic issues.

I den här projektrapporten beskriver vi utvecklingen av det svenska teckenspråkslexikonets exempelmeningar nivågrupperade enligt GERS-skalan, vilket ger bättre stöd för L2-inlärare på olika färdighetsnivåer och underlättar undervisningen i svenskt teckenspråk. För studenterna på kandidatprogrammet för teckenspråk och tolkning används lexikonet som ett digitalt verktyg för att söka och hitta tecken och meningar. Vi såg att det fanns ett behov av att anpassa meningarna till olika kompetensnivåer efter den GERS-baserade skalan från A1 till B2. Fokus på nivåerna A1 till B2 motiveras av de nivåer som uppnåtts inom det 2-åriga programmet. Såvitt vi känner till har ingen annan teckenspråkig lexikal databas tillämpat denna metod – det finns inte heller mycket internationell forskning kring detta – och som sådan är detta en ny modell.

In this project, we describe the development of the Swedish Sign Language online dictionary for including CEFR level evaluations of the example sentences. This is done in order to support L2 learners at different stages of proficiency and facilitate sign language teaching. For the students in the BA program for sign language and interpreting, the dictionary is used as a digital tool for searching and finding signs and sentences. In connection to this, we found that there was a need to adapt the sentences to different levels of proficiency, following the CEFR-based scale from A1 to B2. The focus on levels A1 to B2 is motivated by the levels reached within the 2-year program. As far as we are aware, no other sign language lexical database has applied this method – nor is there much international research pertaining to this – and as such this is a novel model.

Teckenspråkslexikon och -korpusar som flerspråkiga resurser har olika användningsområden, t.ex. andraspråksinlärning (Leeson et al., 2019) och framtida automatisk översättning (Kopf et al., 2021). Svenskt teckenspråkslexikon (Björkstrand, 2008), tillgängligt online sedan 2008, är välbesökt. Föregångarna var Teckenspråk (Österberg, 1916) och Svenskt Teckenspråkslexikon (1997), båda i bokform. I denna artikel presenterar vi följande: a) konstruktion av teckenspråkslexikon, där tekniska lösningar krävs för inspelningar, elicitering och insamling av tecken (Riemer Kankkonen et al., 2018), teckenbeskrivning för bl.a. handform och rörelseriktning, sökfunktioner, publicering i olika applikationer, samt samverkan mellan lexikon och korpus; och b) lexikografiska frågeställningar som lemma, fonologiska varianter, sammansättningar och munrörelser. 

Databasen för Svenskt teckenspråkslexikon består av över 23000 tecken varav 19700 är publicerade. Översättning av tecken till svenska tjänar som grund för s.k. teckenposter, vilket dock inte innebär att varje tecken har en motsvarighet i svenskan. De flesta teckenposter innehåller teckendemonstration, användningsexempel, fotoillustration och teckentranskription baserad på Bergman och Björkstrands (2015) transkriptionssymboler. Vidare har etymologisk beskrivning nyligen lagts till. I lexikonet har sökvägar skapats för att kunna söka på svenska ord, översättning, andra betydelser, alternativa tecken, handform, ämnesområde, m.m. Samverkan mellan lexikon och korpus har resulterat i web-applikationen STS-korpus (Öqvist et al., 2020) och ett nytt korpus- och forskningsbaserat lexikon, STS-signbank, som fortfarande är under konstruktion.

Lexikografiska frågor är väsentliga i utvecklingsarbetet. Ett par exempel på dessa frågor är vilka tecken som är “tillåtna” i lexikonet, hur definiera vad som är ett lexikalt tecken, s.k. kärntecken, och vad som är lemma, d.v.s. grundformen av ett ord/uttryck. Val utifrån manuella komponenter möjliggör sökning baserad på teckenform, medan val av teckenbegrepp utifrån begreppsmässiga, semantiska grunder är inte kopplade till det manuella lemmat (Fenlon et al., 2015; McKee & Vale, 2017; Schermer, 2006). Ytterligare exempel på det utvecklingsarbete som nu pågår är införlivandet av sammansättningar, lexikaliserade avbildande tecken och bokstaveringar i lexikonets befintliga struktur.

In this paper, we describe how we are actively using the Swedish Sign Language (SSL) community in collecting and documenting signs and lexical variation for our language resources, particularly the online Swedish Sign Language Dictionary (SSLD). Apart from using the SSL Corpus as a source of input for new signs and lexical variation in the SSLD, we also involve the community in two ways: first, we interact with SSL signers directly at various venues, collecting signs and judgments about signs; second, we discuss sign usage, lexical variation, and sign formation with SSL signers on social media, particularly through a Facebook group in which we both actively engage in and monitor discussions about SSL. Through these channels, we are able to get direct feedback on our language documentation work and improve on what has become the main lexicographic resource for SSL. We describe the process of simultaneously using corpus data, judgment and elicitation data, and crowdsourcing and discussion groups for enhancing the SSLD, and give examples of findings pertaining to lexical variation resulting from this work.