Research themes
Cooperative & interactive systems
Research in this area exploits Lancaster’s extensive expertise in cooperation between the social sciences and computer science. Our work is focused primarily on three areas:
- Computer-support for cooperative work (CSCW)
- Innovative approaches to user interaction
- Applied natural language processing
Computer-support for cooperative work (CSCW)
We have been involved in research in CSCW since the late 1980s and have established a strong international reputation for its work in social analysis and the development of supporting software for cooperative systems. Our current interests are in developing this work in social and ethnographic analysis and in the development of cooperative environments to support a range of work activities. We are currently engaged in a major project concerned with supporting decision making in meetings which uses large screen technologies and with a project that is developing database visualisation techniques using virtual environments technology.
Innovative approaches to user interaction
New devices and technologies are opening up exciting possibilities for new ways to interact with computer systems. Our work spans a range of technologies from interaction through virtual environments through interaction through palmtops to interaction with wearable computers using physiological parameters such as muscle tension and brain waves. Current projects are concerned with developing toolkits that support interaction with systems using different screen sizes from mobile phones to wall- sized screens, user interaction through virtual worlds and sensor-based interaction with wearable computing systems.
Applied natural language processing
Work in natural language processing is primarily concerned with abstracting information, such as system requirements and management decisions, from natural language documents. Our distinctive probabilistic approach is based on deriving information from large quantities of naturally occurring language - 'corpora'. Probabilistic systems, rather than using hard-and-fast rules, use frequency data along with sophisticated statistical models to make a "best guess" about the correct analysis of a piece of language and often perform at a very high degree of accuracy.