Tools for hierarchical annotation of typed dialogue

Myroslava O. Dzikovska, Charles Callaway, Elaine Farrow

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We discuss a set of tools for annotating a complex hierarchical and linguistic structure of tutorial dialogue based on the NITE XML Toolkit (NXT) (Carletta et al., 2003). The NXT API supports multi-layered stand-off data annotation and synchronisation with timed and speech data. Using NXT, we built a set of extensible tools for detailed structure annotation of typed tutorial dialogue, collected from a tutor and student typing via a chat interface. There are several corpora of tutoring done with such chat-style communication techniques (Shah et al., 2002; Jordan and Siler, 2002), however, our annotation presents a special problem because of its detailed hierarchical structure. We applied our annotation methodology to annotating corpora in two different tutoring domains: basic electricity and electronics, and symbolic differentiation.
    Original languageEnglish
    Title of host publicationProceedings of the 5th Workshop on NLP and XML: Multi-Dimensional Markup in Natural Language Processing
    Place of PublicationStroudsburg, PA, USA
    PublisherAssociation for Computational Linguistics
    Pages57-60
    Number of pages4
    Publication statusPublished - Apr 2006
    Event5th Workshop on NLP and XML: Multi-Dimensional Markup in Natural Language Processing - Trento, Italy
    Duration: 4 Apr 20064 Apr 2006

    Conference

    Conference5th Workshop on NLP and XML: Multi-Dimensional Markup in Natural Language Processing
    CountryItaly
    CityTrento
    Period4/04/064/04/06

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    Dzikovska, M. O., Callaway, C., & Farrow, E. (2006). Tools for hierarchical annotation of typed dialogue. In Proceedings of the 5th Workshop on NLP and XML: Multi-Dimensional Markup in Natural Language Processing (pp. 57-60). Association for Computational Linguistics. http://portal.acm.org/ft_gateway.cfm?id=1621044&type=pdf&CFID=26772021&CFTOKEN=99921861