Publications

Displaying 101 - 117 of 117
  • Senft, G. (1998). Zeichenkonzeptionen in Ozeanien. In R. Posner, T. Robering, & T.. Sebeok (Eds.), Semiotics: A handbook on the sign-theoretic foundations of nature and culture (Vol. 2) (pp. 1971-1976). Berlin: de Gruyter.
  • Seuren, P. A. M. (2000). A discourse-semantic account of topic and comment. In N. Nicolov, & R. Mitkov (Eds.), Recent advances in natural language processing II. Selected papers from RANLP '97 (pp. 179-190). Amsterdam: Benjamins.
  • Seuren, P. A. M. (2000). Pseudocomplementen. In H. Den Besten, E. Elffers, & J. Luif (Eds.), Samengevoegde woorden. Voor Wim Klooster bij zijn afscheid als hoogleraar (pp. 231-237). Amsterdam: Leerstoelgroep Nederlandse Taalkunde, Universiteit van Amsterdam.
  • Seuren, P. A. M. (1998). Towards a discourse-semantic account of donkey anaphora. In S. Botley, & T. McEnery (Eds.), New Approaches to Discourse Anaphora: Proceedings of the Second Colloquium on Discourse Anaphora and Anaphor Resolution (DAARC2) (pp. 212-220). Lancaster: Universiy Centre for Computer Corpus Research on Language, Lancaster University.
  • Silva, S., Petersson, K. M., & Castro, S. (2016). Rhythm in the brain: Is music special? In D. Da Silva Marques, & J. Avila-Toscano (Eds.), Neuroscience to neuropsychology: The study of the human brain (pp. 29-54). Barranquilla, Colombia: Ediciones CUR.
  • De Smedt, K., & Kempen, G. (1987). Incremental sentence production, self-correction, and coordination. In G. Kempen (Ed.), Natural language generation: New results in artificial intelligence, psychology and linguistics (pp. 365-376). Dordrecht: Nijhoff.
  • Smith, A. C., Monaghan, P., & Huettig, F. (2016). Complex word recognition behaviour emerges from the richness of the word learning environment. In K. Twomey, A. C. Smith, G. Westermann, & P. Monaghan (Eds.), Neurocomputational Models of Cognitive Development and Processing: Proceedings of the 14th Neural Computation and Psychology Workshop (pp. 99-114). Singapore: World Scientific. doi:10.1142/9789814699341_0007.

    Abstract

    Computational models can reflect the complexity of human behaviour by implementing multiple constraints within their architecture, and/or by taking into account the variety and richness of the environment to which the human is responding. We explore the second alternative in a model of word recognition that learns to map spoken words to visual and semantic representations of the words’ concepts. Critically, we employ a phonological representation utilising coarse-coding of the auditory stream, to mimic early stages of language development that are not dependent on individual phonemes to be isolated in the input, which may be a consequence of literacy development. The model was tested at different stages during training, and was able to simulate key behavioural features of word recognition in children: a developing effect of semantic information as a consequence of language learning, and a small but earlier effect of phonological information on word processing. We additionally tested the role of visual information in word processing, generating predictions for behavioural studies, showing that visual information could have a larger effect than semantics on children’s performance, but that again this affects recognition later in word processing than phonological information. The model also provides further predictions for performance of a mature word recognition system in the absence of fine-coding of phonology, such as in adults who have low literacy skills. The model demonstrated that such phonological effects may be reduced but are still evident even when multiple distractors from various modalities are present in the listener’s environment. The model demonstrates that complexity in word recognition can emerge from a simple associative system responding to the interactions between multiple sources of information in the language learner’s environment.
  • Stolker, C. J. J. M., & Poletiek, F. H. (1998). Smartengeld - Wat zijn we eigenlijk aan het doen? Naar een juridische en psychologische evaluatie. In F. Stadermann (Ed.), Bewijs en letselschade (pp. 71-86). Lelystad, The Netherlands: Koninklijke Vermande.
  • Sumer, B., & Ozyurek, A. (2016). İşitme Engelli Çocukların Dil Edinimi [Sign language acquisition by deaf children]. In C. Aydin, T. Goksun, A. Kuntay, & D. Tahiroglu (Eds.), Aklın Çocuk Hali: Zihin Gelişimi Araştırmaları [Research on Cognitive Development] (pp. 365-388). Istanbul: Koc University Press.
  • Sumer, B. (2016). Scene-setting and reference introduction in sign and spoken languages: What does modality tell us? In B. Haznedar, & F. N. Ketrez (Eds.), The acquisition of Turkish in childhood (pp. 193-220). Amsterdam: Benjamins.

    Abstract

    Previous studies show that children do not become adult-like in learning to set the scene and introduce referents in their narrations until 9 years of age and even beyond. However, they investigated spoken languages, thus we do not know much about how these skills are acquired in sign languages, where events are expressed in visually similar ways to the real world events, unlike in spoken languages. The results of the current study demonstrate that deaf children (3;5–9;10 years) acquiring Turkish Sign Language, and hearing children (3;8–9;11 years) acquiring spoken Turkish both acquire scene-setting and referent introduction skills at similar ages. Thus the modality of the language being acquired does not have facilitating or hindering effects in the development of these skills.
  • Sumer, B., Zwitserlood, I., Perniss, P., & Ozyurek, A. (2016). Yer Bildiren İfadelerin Türkçe ve Türk İşaret Dili’nde (TİD) Çocuklar Tarafından Edinimi [The acqusition of spatial relations by children in Turkish and Turkish Sign Language (TID)]. In E. Arik (Ed.), Ellerle Konuşmak: Türk İşaret Dili Araştırmaları [Speaking with hands: Studies on Turkish Sign Language] (pp. 157-182). Istanbul: Koç University Press.
  • Suppes, P., Böttner, M., & Liang, L. (1998). Machine Learning of Physics Word Problems: A Preliminary Report. In A. Aliseda, R. van Glabbeek, & D. Westerståhl (Eds.), Computing Natural Language (pp. 141-154). Stanford, CA, USA: CSLI Publications.
  • Van Valin Jr., R. D. (2016). An overview of information structure in three Amazonian languages. In M. Fernandez-Vest, & R. D. Van Valin Jr. (Eds.), Information structure and spoken language from a cross-linguistic perspective (pp. 77-92). Berlin: Mouton de Gruyter.
  • Van Wijk, C., & Kempen, G. (1985). From sentence structure to intonation contour: An algorithm for computing pitch contours on the basis of sentence accents and syntactic structure. In B. Müller (Ed.), Sprachsynthese: Zur Synthese von natürlich gesprochener Sprache aus Texten und Konzepten (pp. 157-182). Hildesheim: Georg Olms.
  • Van Geenhoven, V. (1998). On the Argument Structure of some Noun Incorporating Verbs in West Greenlandic. In M. Butt, & W. Geuder (Eds.), The Projection of Arguments - Lexical and Compositional Factors (pp. 225-263). Stanford, CA, USA: CSLI Publications.
  • Van Valin Jr., R. D. (1998). The acquisition of WH-questions and the mechanisms of language acquisition. In M. Tomasello (Ed.), The new psychology of language: Cognitive and functional approaches to language structure (pp. 221-249). Mahwah, New Jersey: Erlbaum.
  • Zavala, R. (2000). Multiple classifier systems in Akatek (Mayan). In G. Senft (Ed.), Systems of nominal classification (pp. 114-146). Cambridge University Press.

Share this page