@inproceedings{bulla:26021:sign-lang:lrec, author = {Bulla, Jan and Kimmelman, Vadim}, title = {Processing Kinematics of Nonmanual Markers in {R}}, pages = {61--70}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC2026} 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion}, maintitle = {15th International Conference on Language Resources and Evaluation ({LREC} 2026)}, publisher = {{ELRA Language Resources Association (ELRA)}}, address = {Palma, Mallorca, Spain}, day = {16}, month = may, year = {2026}, isbn = {978-2-493814-82-1}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/26021.html}, doi = {10.63317/35fyd7w2c7sm}, abstract = {Nonmanual markers, such as head and eyebrow movements, eye blinks, and mouth shapes, are an important part of natural languages, both spoken and signed. Recent developments in computer vision have made it possible to extract facial and body landmark positions, as well as head-rotation measures, from 2D video recordings, which can be further processed to analyse the kinematics of nonmanual articulators. In this paper, we present an R-based workflow for processing raw outputs of computer vision toolkits with the goal of producing reliable and interpretable kinematic measurements of nonmanual articulators.} } @inproceedings{kimmelman:24008:sign-lang:lrec, author = {Kimmelman, Vadim and Oomen, Marloes and Pfau, Roland}, title = {Headshakes in {NGT}: Relation between Phonetic Properties {\&} Linguistic Functions}, pages = {159--167}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources}, maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {25}, month = may, year = {2024}, isbn = {978-2-493814-30-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24008.html}, doi = {10.63317/4eew3sj6ypi4}, abstract = {Non-manual markers (such as facial expressions and head movements) have been shown to fulfil a wide range of grammatical functions across sign languages. One nonmanual marker that is very wide-spread is headshake used to express negation. While negation and headshake have been studied for a variety of sign languages, phonetic/kinematic research on headshake has been mostly absent. In this paper, we conduct a phonetic analysis of headshake in Sign Language of the Netherlands using a Computer Vision solution, namely OpenFace. We specifically analyze whether linguistic properties of headshake (e.g. spreading and the type of signs co-occurring with the headshake) influence its phonetic form.} } @inproceedings{kimmelman:24009:sign-lang:lrec, author = {Kimmelman, Vadim and Price, Ari and Safar, Josefina and de Vos, Connie and Bulla, Jan}, title = {Nonmanual Marking of Questions in {Balinese} Homesign Interactions: a Computer-Vision Assisted Analysis}, pages = {168--177}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources}, maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {25}, month = may, year = {2024}, isbn = {978-2-493814-30-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24009.html}, doi = {10.63317/5aur9arxz9vb}, abstract = {In recent years, both linguistic resources and computer-based tools have been developed that make it possible to investigate research questions that have not been studied before. In this study, we conduct a study of nonmanual question marking, using data from the Balinese Homesign Corpus -- a unique resource documenting language use in several Balinese homesigners. We further demonstrate how using OpenFace, a Computer-Vision solution, allows for quantitative analysis of head tilts used by these signers in marking questions. We also showcase a pilot statistical analysis of the dynamic kinetic contours of the head movements.} } @inproceedings{susman:24005:sign-lang:lrec, author = {Susman, Margaux and Kimmelman, Vadim}, title = {Eye Blink Detection in Sign Language Data Using {CNNs} and Rule-Based Methods}, pages = {361--369}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources}, maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {25}, month = may, year = {2024}, isbn = {978-2-493814-30-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24005.html}, doi = {10.63317/4oxyyek4f24q}, abstract = {Eye blinks are used in a variety of sign languages as prosodic boundary markers. However, no cross-linguistic quantitative research on eye blinks exists. In order to facilitate such research in future, we develop and test different methods of automatic eyeblink identification, based on a linguistic definition of blinks, and in a dataset of a natural sign language (French Sign Language). We compare two main approaches to eye openness detection: calculating the Eye Aspect Ratio using MediaPipe, and training CNNs to detect openness directly based on images from the video recordings. For the CNN method, we train different models (with different numbers of signers in the training data, different frame crops and different numbers of epochs). We then combine the openness degree detection with a separate rule-based component in order to determine boundaries of blink events. We demonstrate that both methods perform relatively well, and discuss the practical implications of the methods.} } @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}, doi = {10.63317/4or4xpdu7mbk}, 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{kuznetsova:22024:sign-lang:lrec, author = {Kuznetsova, Anna and Imashev, Alfarabi and Mukushev, Medet and Sandygulova, Anara and Kimmelman, Vadim}, title = {Functional Data Analysis of Non-manual Marking of Questions in {Kazakh-Russian} {Sign} {Language}}, pages = {124--131}, 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/22024.html}, doi = {10.63317/3wxdh9h6k4jz}, abstract = {This paper is a continuation of Kuznetsova et al. (2021), which described non-manual markers of polar and wh-questions in comparison with statements in an NLP dataset of Kazakh-Russian Sign Language (KRSL) using Computer Vision. One of the limitations of the previous work was the distortion of the 3D face landmarks when the head was rotated. The proposed solution was to train a simple linear regression model to predict the distortion and then subtract it from the original output. We improve this technique with a multilayer perceptron. Another limitation that we intend to address in this paper is the discrete analysis of the continuous movement of non-manuals. In Kuznetsova et al. (2021) we averaged the value of the non-manual over its scope for statistical analysis. To preserve information on the shape of the movement, in this study we use a statistical tool that is often used in speech research, Functional Data Analysis, specifically Functional PCA.} } @inproceedings{mukushev:22031:sign-lang:lrec, author = {Mukushev, Medet and Kydyrbekova, Aigerim and Kimmelman, Vadim and Sandygulova, Anara}, title = {Towards Large Vocabulary {Kazakh-Russian} {Sign} {Language} Dataset: {KRSL-OnlineSchool}}, pages = {154--158}, 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/22031.html}, doi = {10.63317/4oixy3psku2p}, abstract = {This paper presents a new dataset for Kazakh-Russian Sign Language (KRSL) created for the purposes of Sign Language Processing. In 2020, Kazakhstan's schools were quickly switched to online mode due to the COVID-19 pandemic. Every working day, the El-arna TV channel was broadcasting video lessons for grades from 1 to 11 with sign language translation. This opportunity allowed us to record a corpus with a large vocabulary and spontaneous SL interpretation. To this end, this corpus contains video recordings of Kazakhstan's online school translated to Kazakh-Russian sign language by 7 interpreters. At the moment we collected and cleaned 890 hours of video material. A custom annotation tool was created to make the process of data annotation simple and easy-to-use by the Deaf community. To date, around 325 hours of videos have been annotated with glosses and 4,009 lessons out of 4,547 were transcribed with automatic speech-to-text software. The KRSL-OnlineSchool dataset will be made publicly available at https://krslproject.github.io/online-school/} } @inproceedings{mukushev:22030:sign-lang:lrec, author = {Mukushev, Medet and Sabyrov, Arman and Sultanova, Madina and Kimmelman, Vadim and Sandygulova, Anara}, title = {Towards Semi-automatic Sign Language Annotation Tool: {SLAN-tool}}, pages = {159--164}, 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/22030.html}, doi = {10.63317/2tgdrajbv458}, abstract = {This paper presents a semi-automatic annotation tool for sign languages namely SLAN-tool. The SLAN-tool provides a web-based service for the annotation of sign language videos. Researchers can use the SLAN-tool web service to annotate new and existing sign language datasets with different types of annotations, such as gloss, handshape configurations, and signing regions. This is allowed using a custom tier adding functionality. A unique feature of the tool is its automatic annotation functionality which uses several neural network models in order to recognize signing segments from videos and classify handshapes according to HamNoSys handshape inventory. Furthermore, SLAN-tool users can export annotations and import them into ELAN. The SLAN-tool is publicly available at https://slan-tool.com.} } @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}, doi = {10.63317/4fzv7aim6uv3}, 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{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: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{mukushev-etal-2022-crowdsourcing:lrec, author = {Mukushev, Medet and Ubingazhibov, Aidyn and Kydyrbekova, Aigerim and Imashev, Alfarabi and Kimmelman, Vadim and Sandygulova, Anara}, title = {Crowdsourcing {Kazakh-Russian} {Sign} {Language}: {FluentSigners-50}}, pages = {2541--2547}, editor = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric 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 Odijk, Jan and Piperidis, Stelios}, booktitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {20--25}, month = jun, year = {2022}, isbn = {979-10-95546-72-6}, language = {english}, url = {https://aclanthology.org/2022.lrec-1.271}, doi = {10.63317/3azsxoosxde5}, abstract = {This paper presents the methodology we used to crowdsource a data collection of a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) created for Sign Language Processing. By involving the Deaf community throughout the research process, we firstly designed a research protocol and then performed an efficient crowdsourcing campaign that resulted in a new FluentSigners-50 dataset. The FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers for 43,250 video samples. Dataset contributors recorded videos in real-life settings on various backgrounds using various devices such as smartphones and web cameras. Therefore, each dataset contribution has a varying distance to the camera, camera angles and aspect ratio, video quality, and frame rates. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life signed speech. FluentSigners-50 is publicly available at https://krslproject.github.io/fluentsigners-50/} } @inproceedings{mukushev-etal-2020-evaluation:lrec, author = {Mukushev, Medet and Sabyrov, Arman and Imashev, Alfarabi and Koishybay, Kenessary and Kimmelman, Vadim and Sandygulova, Anara}, title = {Evaluation of Manual and Non-manual Components for Sign Language Recognition}, pages = {6073--6078}, 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-1.745}, doi = {10.63317/5bj5d22wctiy}, abstract = {The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign's recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2{\%} of accuracy for dataset with 20 signs and 77.9{\%} of accuracy for dataset with 2 classes (statement vs question). Dataset can be downloaded from the following website: https://krslproject.github.io/krsl20/} } @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}, doi = {10.63317/5jd6s9ekbgvt}, 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.} }