Displaying 201 - 207 of 207
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Wittenburg, P., Brugman, H., Broeder, D., & Russel, A. (2004). XML-based language archiving. In Workshop Proceedings on XML-based Richly Annotaded Corpora (LREC2004) (pp. 63-69). Paris: ELRA - European Language Resources Association.
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Wittenburg, P., Gulrajani, G., Broeder, D., & Uneson, M. (2004). Cross-disciplinary integration of metadata descriptions. In M. Lino, M. Xavier, F. Ferreira, R. Costa, & R. Silva (
Eds. ), Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC2004) (pp. 113-116). Paris: ELRA - European Language Resources Association. -
Wittenburg, P., Johnson, H., Buchhorn, M., Brugman, H., & Broeder, D. (2004). Architecture for distributed language resource management and archiving. In M. Lino, M. Xavier, F. Ferreira, R. Costa, & R. Silva (
Eds. ), Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC2004) (pp. 361-364). Paris: ELRA - European Language Resources Association. -
Zhang, Y., Yurovsky, D., & Yu, C. (2015). Statistical word learning is a continuous process: Evidence from the human simulation paradigm. In D. Noelle, R. Dale, A. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (
Eds. ), Proceedings of the 37th Annual Meeting of the Cognitive Science Society (CogSci 2015) (pp. 2422-2427). Austin: Cognitive Science Society.Abstract
In the word-learning domain, both adults and young children are able to find the correct referent of a word from highly ambiguous contexts that involve many words and objects by computing distributional statistics across the co-occurrences of words and referents at multiple naming moments (Yu & Smith, 2007; Smith & Yu, 2008). However, there is still debate regarding how learners accumulate distributional information to learn object labels in natural learning environments, and what underlying learning mechanism learners are most likely to adopt. Using the Human Simulation Paradigm (Gillette, Gleitman, Gleitman & Lederer, 1999), we found that participants’ learning performance gradually improved and that their ability to remember and carry over partial knowledge from past learning instances facilitated subsequent learning. These results support the statistical learning model that word learning is a continuous process. -
Zinn, C., Cablitz, G., Ringersma, J., Kemps-Snijders, M., & Wittenburg, P. (2008). Constructing knowledge spaces from linguistic resources. In Proceedings of the CIL 18 Workshop on Linguistic Studies of Ontology: From lexical semantics to formal ontologies and back.
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Zinn, C. (2008). Conceptual spaces in ViCoS. In S. Bechhofer, M. Hauswirth, J. Hoffmann, & M. Koubarakis (
Eds. ), The semantic web: Research and applications (pp. 890-894). Berlin: Springer.Abstract
We describe ViCoS, a tool for constructing and visualising conceptual spaces in the area of language documentation. ViCoS allows users to enrich existing lexical information about the words of a language with conceptual knowledge. Their work towards language-based, informal ontology building must be supported by easy-to-use workflows and supporting software, which we will demonstrate. -
Zwitserlood, I., Ozyurek, A., & Perniss, P. M. (2008). Annotation of sign and gesture cross-linguistically. In O. Crasborn, E. Efthimiou, T. Hanke, E. D. Thoutenhoofd, & I. Zwitserlood (
Eds. ), Construction and Exploitation of Sign Language Corpora. 3rd Workshop on the Representation and Processing of Sign Languages (pp. 185-190). Paris: ELDA.Abstract
This paper discusses the construction of a cross-linguistic, bimodal corpus containing three modes of expression: expressions from two sign languages, speech and gestural expressions in two spoken languages and pantomimic expressions by users of two spoken languages who are requested to convey information without speaking. We discuss some problems and tentative solutions for the annotation of utterances expressing spatial information about referents in these three modes, suggesting a set of comparable codes for the description of both sign and gesture. Furthermore, we discuss the processing of entered annotations in ELAN, e.g. relating descriptive annotations to analytic annotations in all three modes and performing relational searches across annotations on different tiers.
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