Publications

Displaying 401 - 406 of 406
  • Wittenburg, P., & Trilsbeek, P. (2010). Digital archiving - a necessity in documentary linguistics. In G. Senft (Ed.), Endangered Austronesian and Australian Aboriginal languages: Essays on language documentation, archiving and revitalization (pp. 111-136). Canberra: Pacific Linguistics.
  • Wittenburg, P., Trilsbeek, P., & Lenkiewicz, P. (2010). Large multimedia archive for world languages. In SSCS'10 - Proceedings of the 2010 ACM Workshop on Searching Spontaneous Conversational Speech, Co-located with ACM Multimedia 2010 (pp. 53-56). New York: Association for Computing Machinery, Inc. (ACM). doi:10.1145/1878101.1878113.

    Abstract

    In this paper, we describe the core pillars of a large archive oflanguage material recorded worldwide partly about languages that are highly endangered. The bases for the documentation of these languages are audio/video recordings which are then annotated at several linguistic layers. The digital age completely changed the requirements of long-term preservation and it is discussed how the archive met these new challenges. An extensive solution for data replication has been worked out to guarantee bit-stream preservation. Due to an immediate conversion of the incoming data to standards -based formats and checks at upload time lifecycle management of all 50 Terabyte of data is widely simplified. A suitable metadata framework not only allowing users to describe and discover resources, but also allowing them to organize their resources is enabling the management of this amount of resources very efficiently. Finally, it is the Language Archiving Technology software suite which allows users to create, manipulate, access and enrich all archived resources given that they have access permissions.
  • Wittenburg, P., Bel, N., Borin, L., Budin, G., Calzolari, N., Hajicova, E., Koskenniemi, K., Lemnitzer, L., Maegaard, B., Piasecki, M., Pierrel, J.-M., Piperidis, S., Skadina, I., Tufis, D., Van Veenendaal, R., Váradi, T., & Wynne, M. (2010). Resource and service centres as the backbone for a sustainable service infrastructure. In N. Calzolari, B. Maegaard, J. Mariani, J. Odjik, K. Choukri, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10) (pp. 60-63). European Language Resources Association (ELRA).

    Abstract

    Currently, research infrastructures are being designed and established in manydisciplines since they all suffer from an enormous fragmentation of theirresources and tools. In the domain of language resources and tools the CLARINinitiative has been funded since 2008 to overcome many of the integration andinteroperability hurdles. CLARIN can build on knowledge and work from manyprojects that were carried out during the last years and wants to build stableand robust services that can be used by researchers. Here service centres willplay an important role that have the potential of being persistent and thatadhere to criteria as they have been established by CLARIN. In the last year ofthe so-called preparatory phase these centres are currently developing four usecases that can demonstrate how the various pillars CLARIN has been working oncan be integrated. All four use cases fulfil the criteria of beingcross-national.
  • Zavala, R. (2000). Multiple classifier systems in Akatek (Mayan). In G. Senft (Ed.), Systems of nominal classification (pp. 114-146). Cambridge University Press.
  • 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., Wittenburg, P., & Ringersma, J. (2010). An evolving eScience environment for research data in linguistics. In N. Calzolari, B. Maegaard, J. Mariani, J. Odjik, K. Choukri, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10) (pp. 894-899). European Language Resources Association (ELRA).

    Abstract

    The amount of research data in the Humanities is increasing at fastspeed. Metadata helps describing and making accessible this data tointerested researchers within and across institutions. While metadatainteroperability is an issue that is being recognised and addressed,the systematic and user-driven provision of annotations and thelinking together of resources into new organisational layers havereceived much less attention. This paper gives an overview of ourevolving technological eScience environment to support suchfunctionality. It describes two tools, ADDIT and ViCoS, which enableresearchers, rather than archive managers, to organise and reorganiseresearch data to fit their particular needs. The two tools, which areembedded into our institute's existing software landscape, are aninitial step towards an eScience environment that gives our scientistseasy access to (multimodal) research data of their interest, andempowers them to structure, enrich, link together, and share such dataas they wish.

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