Natural language use, acquisition, and understanding takes place usually in multisensory and multimedia communication environments. Therefore, for one to model language in its interaction and integration with sensorimotor experiences, one needs a representative corpus of such interplay. In this paper, we will present the first corpus of language use and sensorimotor experience recordings in everyday human:human interaction, in which spontaneous language communication has been recorded along with corresponding multiview video recordings, recordings of 3D full body kinematics, and 3D tracking of objects in focus. It is a twelve-hour corpus which comprises of six everyday human:human interaction scenes, each one performed 3 times by 4 different English-speaking couples (interaction between a male and a female actor), each couple acting each scene in two settings: a fully naturalistic setting in which 5-camera multi-view video recordings take place, and a high-tech setting, with full body motion capture for both individuals, a 2-camera multiview video recording, and 3D tracking of focus objects. The corpus has been developed within an EU-funded cognitive systems research project, POETICON (http://www.poeticon.eu), and represents a new type of language resources for cognitive systems. Namely, a corpus that reveals the dynamic role of language in its interplay with sensorimotor experiences and which allows one to computationally model this interplay.

In this paper, we look into the notion of cross-media decision mechanisms, focussing on ones that work within multimedia documents for a variety of applications, such as the generation of intelligent multimedia presentations and multimedia indexing. In order for these mechanisms to go beyond the identification of semantic equivalence relations between media, which is what integration does, appropriate corpora and annotations are needed. Drawing from our experience in the REVEAL THIS project, we indicate the characteristics that such corpora should have, and suggest a number of annotations that would allow for training/designing such mechanisms. We conclude with a view on the suitability of two related markup languages (MPEG-7 and EMMA) for accommodating the suggested annotations.