Human-Centered Compression for Collaborative Text Input

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Increasingly, users of information appliances -- cell phones, personal digital assistants, and the like -- are using them to communicate textual information, and finding them difficult to interact with. The properties that make the devices useful in the first place (size, mobility, embeddedness) are exactly the properties that make inputting text difficult. The principal investigator is developing ways to make use of the intrinsic redundancy of natural language to dramatically improve the efficiency with which natural language text can be entered into such devices. This redundancy accounts, for instance, for our ability to read compressed text such as "Cn u rd ths?". If information appliances could also read such highly abbreviated text with high reliability, abbreviated text could be used to speed up text input.Developing systems to decode text compressed in such a "human-centered" manner requires advances in modeling of the compression method and in user interface techniques that allow interactive correction of any mistakes in the computer's understanding of the compressed text. This research addresses the required advances by exploiting statistical language modeling techniques to address the challenges involved in minimizing cognitive load on the user. Such an ability would dramatically simplify and expand the process of communicating with and through computer and telecommunications system using text, and expand the base of potential environments where text input could occur and users who could participate. Similarly, benefits would accrue to disabled users and others operating under degraded conditions.