Publications

Displaying 201 - 205 of 205
  • Wittenburg, P., van Kuijk, D., & Dijkstra, T. (1996). Modeling human word recognition with sequences of artificial neurons. In C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, & B. Sendhoff (Eds.), Artificial Neural Networks — ICANN 96. 1996 International Conference Bochum, Germany, July 16–19, 1996 Proceedings (pp. 347-352). Berlin: Springer.

    Abstract

    A new psycholinguistically motivated and neural network based model of human word recognition is presented. In contrast to earlier models it uses real speech as input. At the word layer acoustical and temporal information is stored by sequences of connected sensory neurons which pass on sensor potentials to a word neuron. In experiments with a small lexicon which includes groups of very similar word forms, the model meets high standards with respect to word recognition and simulates a number of wellknown psycholinguistical effects.
  • Xiao, M., Kong, X., Liu, J., & Ning, J. (2009). TMBF: Bloom filter algorithms of time-dependent multi bit-strings for incremental set. In Proceedings of the 2009 International Conference on Ultra Modern Telecommunications & Workshops.

    Abstract

    Set is widely used as a kind of basic data structure. However, when it is used for large scale data set the cost of storage, search and transport is overhead. The bloom filter uses a fixed size bit string to represent elements in a static set, which can reduce storage space and search cost that is a fixed constant. The time-space efficiency is achieved at the cost of a small probability of false positive in membership query. However, for many applications the space savings and locating time constantly outweigh this drawback. Dynamic bloom filter (DBF) can support concisely representation and approximate membership queries of dynamic set instead of static set. It has been proved that DBF not only possess the advantage of standard bloom filter, but also has better features when dealing with dynamic set. This paper proposes a time-dependent multiple bit-strings bloom filter (TMBF) which roots in the DBF and targets on dynamic incremental set. TMBF uses multiple bit-strings in time order to present a dynamic increasing set and uses backward searching to test whether an element is in a set. Based on the system logs from a real P2P file sharing system, the evaluation shows a 20% reduction in searching cost compared to DBF.
  • 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.
  • 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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