• Argument Structure of Classifier Predicates : Canonical and Non-canonical Mappings in Four Sign Languages 

      Kimmelman, Vadim; De Lint, Vanja; De Vos, Connie; Oomen, Marloes; Pfau, Roland; Vink, Lianne; Aboh, Enoch (Peer reviewed; Journal article, 2019-08-08)
      We analyze argument structure of whole-entity and handling classifier predicates in four sign languages (Russian Sign Language, Sign Language of the Netherlands, German Sign Language, and Kata Kolok) using parallel datasets ...
    • Argument structure of classifier predicates in Russian Sign Language 

      Kimmelman, Vadim; Pfau, Roland; Aboh, Enoch (Peer reviewed; Journal article, 2019-04-03)
      We analyze classifier predicates in Russian Sign Language (RSL) using a combination of naturalistic corpus and elicited data in order to determine their argument structure, and to test the generalization, based on research ...
    • Automatic Classification of Handshapes in Russian Sign Language 

      Mukushev, Medet; Imashev, Alfarabi; Kimmelman, Vadim; Sandygulova, Anara (Chapter, 2020)
      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 ...
    • Body-anchored verbs and argument omission in two sign languages 

      Oomen, Marloes; Kimmelman, Vadim (Peer reviewed; Journal article, 2019-03-26)
      Using quantitative methods, we analyze naturalistic corpus data in two sign languages, German Sign Language and Russian Sign Language, to study subject-omission patterns. We find that, in both languages, the interpretation ...
    • Evaluation of Manual and Non-manual Components for Sign Language Recognition 

      Mukushev, Medet; Sabyrov, Arman; Imashev, Alfarabi; Koishibay, Kenessary; Kimmelman, Vadim; Sandygulova, Anara (Chapter, 2020)
      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 ...
    • Exploring Networks of Lexical Variation in Russian Sign Language 

      Kimmelman, Vadim; Komarova, Anna; Luchkova, Lyudmila; Vinogradova, Valeria; Alekseeva, Oksana (Journal article; Peer reviewed, 2022)
      When describing variation at the lexical level in sign languages, researchers often distinguish between phonological and lexical variants, using the following principle: if two signs differ in only one of the major ...
    • Eyebrow position in grammatical and emotional expressions in Kazakh-Russian Sign Language: A quantitative study 

      Kimmelman, Vadim; Imashev, Alfarabi; Mukushev, Medet; Sandygulova, Anara (Journal article; Peer reviewed, 2020-06)
      Facial expressions in sign languages are used to express grammatical functions, such as question marking, but can also be used to express emotions (either the signer’s own or in constructed action contexts). Emotions and ...
    • Functional Data Analysis of Non-manual Marking of Questions in Kazakh-Russian Sign Language 

      Kuznetsova, Anna; Imashev, Alfarabi; Mukushev, Medet; Sandygulova, Anara; Kimmelman, Vadim (Chapter, 2022)
      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 ...
    • Information structure: theoretical perspectives 

      Kimmelman, Vadim; Pfau, Roland (Chapter, 2021)
      This chapter discusses the terminology commonly used in the information structure literature: in particular, topic, focus, contrast, and emphasis. An important component of our discussion is the impact of the visual-gestural ...
    • K-RSL: a Corpus for Linguistic Understanding, Visual Evaluation, and Recognition of Sign Languages 

      Imashev, Alfarabi; Mukushev, Medet; Kimmelman, Vadim; Sandygulova, Anara (Chapter, 2020)
      The paper presents the first dataset that aims to serve interdisciplinary purposes for the utility of computer vision community and sign language linguistics. To date, a majority of Sign Language Recognition (SLR) approaches ...
    • Lexical expression of time in Russian Sign Language 

      Burkova, Svetlana; Filimonova, Elizaveta; Kimmelman, Vadim; Kopylova, Viktoriia; Semushina, Nina (Peer reviewed; Journal article, 2019)
      We studied lexical expressions of time in Russian Sign Language (RSL). We collected lexical signs describing a variety of time-related concepts produced by fifteen RSL signers from different regions. We then analyzed ...
    • Phonetics of Negative Headshake in Russian Sign Language: A Small-Scale Corpus Study 

      Chizhikova, Anastasia; Kimmelman, Vadim (Chapter, 2022)
      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 ...
    • Quantification: theoretical perspectives 

      Kimmelman, Vadim; Quer, Josep (Chapter, 2021)
      The study of quantificational expressions is one of the central domains in the field of natural language semantics. Probably every language has means of expressing quantification, but quantifiers in natural languages are ...
    • Question-answer pairs in Russian Sign Language: A corpus study 

      Khristoforova, Evgeniia; Kimmelman, Vadim (Journal article, 2021)
      We describe basic morphosyntactic and semantic properties of question-answer pairs (QAPs) collected from the online corpus of Russian Sign Language (RSL). We identified two classes of QAPs: classical and discourse QAPs, ...
    • Transitivity prominence within and across modalities 

      Börstell, Carl; Jantunen, Tommi; Kimmelman, Vadim; De Lint, Vanja; Mesch, Johanna; Oomen, Marloes (Peer reviewed; Journal article, 2019-12-31)
      We investigate transitivity prominence of verbs across signed and spoken languages, based on data from both valency dictionaries and corpora. Our methodology relies on the assumption that dictionary data and corpus-based ...