@inproceedings{chizhikova:22011:sign-lang:lrec, author = {Chizhikova, Anastasia and Kimmelman, Vadim}, title = {Phonetics of Negative Headshake in {Russian} {Sign} {Language}: A Small-Scale Corpus Study}, pages = {29--36}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources}, maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {25}, month = jun, year = {2022}, isbn = {979-10-95546-86-3}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22011.html}, abstract = {We analyzed negative headshake found in the online corpus of Russian Sign Language. We found that negative headshake can co-occur with negative manual signs, although most of these signs are not accompanied by it. We applied OpenFace, a Computer Vision toolkit, to extract head rotation measurements from video recordings, and analyzed the headshake in terms of the number of peaks (turns), the amplitude of the turns, and their frequency. We find that such basic phonetic measurements of headshake can be extracted using a combination of manual annotation and Computer Vision, and can be further used in comparative research across constructions and sign languages.} } @inproceedings{kuder:22020:sign-lang:lrec, author = {Kuder, Anna}, title = {Making Sign Language Corpora Comparable: A Study of Palm-Up and Throw-Away in {Polish} {Sign} {Language}, {German} {Sign} {Language}, and {Russian} {Sign} {Language}}, pages = {110--117}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources}, maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {25}, month = jun, year = {2022}, isbn = {979-10-95546-86-3}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22020.html}, abstract = {This paper is primarily devoted to describing the preparation phase of a large-scale comparative study based on naturalistic linguistic data drawn from multiple sign language corpora. To provide an example, I am using my current project on manual gestural elements in Polish Sign Language, German Sign Language, and Russian Sign Language. The paper starts with a description of the reasons behind undertaking this project. Then, I describe the scope of my study, which is focused on two manual elements present in all three mentioned sign languages: palm-up and throw-away; and the three corpora which are my data sources. This is followed by a presentation of the steps taken in the initial stages of the project in order to make the data comparable. Those steps are: choosing the adequate data samples from all three corpora, gathering all data within the chosen software, and creating an annotation schema that builds on the annotations already present in all three corpora. Even though the project is still underway, and the annotation process is ongoing, preliminary discussions about the nature of the analysed manual activities are presented based on the initial annotations for the sake of evaluating the created annotation schema. I conclude the paper with some remarks about the performance of the employed methodology.} } @inproceedings{mukushev:20036:sign-lang:lrec, author = {Mukushev, Medet and Imashev, Alfarabi and Kimmelman, Vadim and Sandygulova, Anara}, title = {Automatic Classification of Handshapes in {Russian} {Sign} {Language}}, pages = {165--170}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna}, booktitle = {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}, maintitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {16}, month = may, year = {2020}, isbn = {979-10-95546-54-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/20036.html}, abstract = {Handshapes are one of the basic parameters of signs, and any phonological or phonetic analysis of a sign language must account for handshapes. Many sign languages have been carefully analysed by sign language linguists to create handshape inventories. This has theoretical implications, but also applied use, as it is important due to the need of generating corpora for sign languages that can be searched, filtered, sorted by different sign components (such as handshapes, orientation, location, movement, etc.). However, it is a very time-consuming process, thus only a handful of sign languages have such inventories. This work proposes a process of automatically generating such inventories for sign languages by applying automatic hand detection, cropping, and clustering techniques. We applied our proposed method to a commonly used resource: the Spreadthesign online dictionary (www.spreadthesign.com), in particular to Russian Sign Language (RSL). We then manually verified the data to be able to perform classification. Thus, the proposed pipeline can serve as an alternative approach to manual annotation, and can help linguists in answering numerous research questions in relation to handshape frequencies in sign languages.} } @inproceedings{kagirov-etal-2020-theruslan:lrec, author = {Kagirov, Ildar and Ivanko, Denis and Ryumin, Dmitry and Axyonov, Alexander and Karpov, Alexey}, title = {{T}he{R}u{SL}an: Database of {R}ussian {S}ign {L}anguage}, pages = {6079--6085}, editor = {Calzolari, Nicoletta and Fr{\'e}d{\'e}ric B{\'e}chet and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios}, booktitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {11--16}, month = may, year = {2020}, isbn = {979-10-95546-34-4}, language = {english}, url = {https://aclanthology.org/2020.lrec-1.746}, abstract = {In this paper, a new Russian sign language multimedia database TheRuSLan is presented. The database includes lexical units (single words and phrases) from Russian sign language within one subject area, namely, ``food products at the supermarket'', and was collected using MS Kinect 2.0 device including both FullHD video and the depth map modes, which provides new opportunities for the lexicographical description of the Russian sign language vocabulary and enhances research in the field of automatic gesture recognition. Russian sign language has an official status in Russia, and over 120,000 deaf people in Russia and its neighboring countries use it as their first language. Russian sign language has no writing system, is poorly described and belongs to the low-resource languages. The authors formulate the basic principles of annotation of sign words, based on the collected data, and reveal the content of the collected database. In the future, the database will be expanded and comprise more lexical units. The database is explicitly made for the task of creating an automatic system for Russian sign language recognition.} } @inproceedings{kimmelman:18010:sign-lang:lrec, author = {Kimmelman, Vadim and Khristoforova, Evegniia}, title = {Quotation in {Russian} {Sign} {Language}: A Corpus Study}, pages = {91--94}, editor = {Bono, Mayumi and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Osugi, Yutaka}, booktitle = {Proceedings of the {LREC2018} 8th Workshop on the Representation and Processing of Sign Languages: Involving the Language Community}, maintitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Miyazaki, Japan}, day = {12}, month = may, year = {2018}, isbn = {979-10-95546-01-6}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/18010.html}, abstract = {We studied how quotation is expressed in naturalistic discourse in Russian Sign Language (RSL). We studied a sub-corpus of the online corpus of RSL containing narratives by eleven signers from Moscow. We identified 341 instances of quotation, including reported speech and reported thoughts. We annotated syntactic, semantic, and prosodic properties of the found instances of quotation. We found out that quotative constructions in RSL have the same basic structure as similar constructions in other spoken and signed languages. Furthermore, similarly to quotation in other sign languages, quotation in RSL can be marked by head and/or body movement and change in eye gaze direction. However, all of these markers are clearly optional, and a considerable number of examples do not include any of these markers. Furthermore, we found that, judging by the behavior of indexicals, RSL narratives in our dataset have a very strong preference for using direct speech. We discuss theoretical implications of the RSL data to the theory of quotation in sign languages.} } @inproceedings{kimmelman:18011:sign-lang:lrec, author = {Kimmelman, Vadim and Klomp, Ulrika and Oomen, Marloes}, title = {Where Methods Meet: Combining Corpus Data and Elicitation in Sign Language Research}, pages = {95--100}, editor = {Bono, Mayumi and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Osugi, Yutaka}, booktitle = {Proceedings of the {LREC2018} 8th Workshop on the Representation and Processing of Sign Languages: Involving the Language Community}, maintitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Miyazaki, Japan}, day = {12}, month = may, year = {2018}, isbn = {979-10-95546-01-6}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/18011.html}, abstract = {We discuss three case studies on various grammatical phenomena in Russian Sign Language (RSL) and Sign Language of the Netherlands (NGT) in order to compare corpus-based and elicitation-based approaches to sign linguistics. Firstly, we investigate impersonal reference in RSL using corpus search, informal elicitation, and an acceptability judgment task. Secondly, we examine argument structure and pro-drop licensing in NGT psych verb constructions using corpus search and a supplementary acceptability judgment task. Thirdly, we investigate conditional clauses in NGT based on corpus search, and contrast the findings with those from elicitation-based studies of conditional clauses in other sign languages. The three case studies highlight both the merits and limitations of combining different research methods as well as illustrate some of the issues that arise from doing so -- and how they may be navigated. We conclude that corpus-based research serves to identify the boundaries of observed variation and describe both expected and unexpected patterns, while the underlying factors for these patterns can be investigated by eliciting data in more controlled contexts. Finally, we demonstrate that the differences in the results obtained via various research methods have important practical implications, in particular for sign language education.} } @inproceedings{kimmelman-etal-2018-ipsl:lrec, author = {Kimmelman, Vadim and Klezovich, Anna and Moroz, George}, title = {{IPSL}: A Database of Iconicity Patterns in Sign Languages. Creation and Use}, pages = {4230--4234}, editor = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu}, booktitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Miyazaki, Japan}, day = {7--12}, month = may, year = {2018}, isbn = {979-10-95546-00-9}, language = {english}, url = {https://aclanthology.org/L18-1667}, abstract = {We created the first large-scale database of signs annotated according to various parameters of iconicity. The signs represent concrete concepts in seven semantic fields in nineteen sign languages; 1542 signs in total. Each sign was annotated with respect to the type of form-image association, the presence of iconic location and movement, personification, and with respect to whether the sign depicts a salient part of the concept. We also created a website: https://sl-iconicity.shinyapps.io/iconicity patterns/ with several visualization tools to represent the data from the database. It is possible to visualize iconic properties of separate concepts or iconic properties of semantic fields on the map of the world, and to build graphs representing iconic patterns for selected semantic fields. A preliminary analysis of the data shows that iconicity patterns vary across semantic fields and across languages. The database and the website can be used to further study a variety of theoretical questions related to iconicity in sign languages.} } @inproceedings{yu-etal-2018-sign:lrec, author = {Yu, Shi and Geraci, Carlo and Abner, Natasha}, title = {Sign Languages and the Online World Online Dictionaries {\&} Lexicostatistics}, pages = {4235--4240}, editor = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu}, booktitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Miyazaki, Japan}, day = {7--12}, month = may, year = {2018}, isbn = {979-10-95546-00-9}, language = {english}, url = {https://aclanthology.org/L18-1668}, abstract = {Several online dictionaries documenting the lexicon of a variety of sign languages (SLs) are now available. These are rich resources for comparative studies, but there are methodological issues that must be addressed regarding how these resources are used for research purposes. We created a web-based tool for annotating the articulatory features of signs (handshape, location, movement and orientation). Videos from online dictionaries may be embedded in the tool, providing a mechanism for large-scale theoretically-informed sign language annotation. Annotations are saved in a spreadsheet format ready for quantitative and qualitative analyses. Here, we provide proof of concept for the utility of this tool in linguistic analysis. We used the SL adaptation of the Swadesh list (Woodward, 2000) and applied lexicostatistic and phylogenetic methods to a sample of 23 SLs coded using the web-based tool; supplementary historic information was gathered from the Ethnologue of World Languages and other online sources. We report results from the comparison of all articulatory features for four Asian SLs (Chinese, Hong Kong, Taiwanese and Japanese SLs) and from the comparison of handshapes on the entire 23 language sample. Handshape analysis of the entire sample clusters all Asian SLs together, separated from the European, American, and Brazilian SLs in the sample, as historically expected. Within the Asian SL cluster, analyses also show, for example, marginal relatedness between Chinese and Hong Kong SLs.} } @inproceedings{kimmelman:16018:sign-lang:lrec, author = {Kimmelman, Vadim}, title = {Transitivity in {RSL}: a corpus-based account}, pages = {117--120}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna}, booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining}, maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Portoro{\v z}, Slovenia}, day = {28}, month = may, year = {2016}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/16018.html}, abstract = {A recent typological study of transitivity Haspelmath (2015) demonstrated that verbs can be ranked according to transitivity prominence, that is, according to how likely they are to be transitive cross-linguistically. This ranking can be argued to be cognitively rooted (based on the properties of the events and their participants) or frequency-related (based on the frequency of different types of events in the real world). Both types of explanation imply that the transitivity ranking should apply across modalities. To test it, we analysed transitivity of frequent verbs in the corpus of Russian Sign Language by calculating the proportion of overt direct and indirect objects and clausal complements. We found that transitivity as expressed by the proportion of overt direct objects is highly positively correlated with the transitive prominence determined cross-linguistically. We thus confirmed the modality-independent nature of transitivity ranking.} } @inproceedings{karpov:12012:sign-lang:lrec, author = {Karpov, Alexey and {\v Z}elezn{\'y}, Milo{\v s}}, title = {Towards {Russian} {Sign} {Language} Synthesizer: Lexical Level}, pages = {83--86}, editor = {Crasborn, Onno and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Kristoffersen, Jette and Mesch, Johanna}, booktitle = {Proceedings of the {LREC2012} 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon}, maintitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Istanbul, Turkey}, day = {27}, month = may, year = {2012}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/12012.html}, abstract = {In this paper, we present a survey of existing Russian sign language electronic and printed resources and dictionaries. The problem of differences in dialects of Russian sign language used in various local communities of Russia and some other CIS countries is discussed in the paper. Also the first version of a computer system for synthesis of elements of Russian sign language (signed Russian and fingerspelling) is presented in the given paper. It is a universal multi-modal synthesizer both for Russian spoken language and signed Russian that is based on a model of animated 3D signing avatar. The proposed system inputs data in the text form and converts them into the audio-visual modality, synchronizing visual manual gestures and articulation with audio speech signal. Generated audio-visual signed Russian speech and spoken language is a fusion of dynamic gestures shown by the avatar{\'i}s both hands, lip movements articulating words and auditory speech, so the multimodal output is available both for the deaf and hearing-able people.} } @inproceedings{voskresenskiy:10031:sign-lang:lrec, author = {Voskresenskiy, Alexander and Ilyin, Sergey}, title = {About Recognition of Sign Language Gestures}, pages = {247--250}, editor = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam}, booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies}, maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Valletta, Malta}, day = {22--23}, month = may, year = {2010}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10031.html}, abstract = {A motion capture technique for implementing sign language dictionary is described. Problems of perception and recognition of gestures of Russian sign language in system of the automated sign language translation are discussed. The new approach to morphology of gestures and a method for separate gestures in sign statements are offered. The working definition for "text understanding" is offered.} }