Traditionally a corpus is a collection of language examples: written or spoken examples of words, sentences, phrases or texts. Nowadays a corpus can be any collection of examples, for example, human-human interactions, protoin interaction, video fragments, maintenance information, etc. A corpus is collected in order to learn from it, that is, to extract domain-specific information. Examples can be analysed and rules and models underlying the examples can be discovered. Machine learning algorithms are used to extract relationships between examples. Manual structuring of such data (annotation) allows the integration of human preferences and knowledge in machine learning algorithms.
Published in Chapter:
Creating Social Technologies to Assist and Understand Social Interactions
Anton Nijholt (University of Twente, The Netherlands), Dirk Heylen (University of Twente, The Netherlands), and Rutger Rienks (University of Twente, The Netherlands)
Copyright: © 2009
|Pages: 13
DOI: 10.4018/978-1-60566-264-0.ch028
Abstract
In this chapter the authors discuss a particular approach to the creation of socio-technical systems for the meeting domain. Besides presenting a methodology this chapter will present applications that have been constructed on the basis of the method and applications that can be envisioned. Throughout the chapter, illustrations are drawn from research on the development of meeting support tools. The chapter concludes with a section on implications and considerations for the on-going development of social technical systems in general and for the meeting domain in particular.