In this article, we describe the latest version of Sibylle, an AAC system that permits persons who have severe physical disabilities to enter text with any computer application, as well as to compose messages to be read out through speech synthesis. The system consists of a virtual keyboard comprising a set of keypads that allow for the entering of characters or full words by a single-switch selection process. It also includes a sophisticated word prediction component which dynamically calculates the most appropriate words for a given context. This component is auto-adaptive, that is, it learns with every text the user enters. It thus adapts its predictions to the user's language and the current topic of communication as well. So far, the system works for French, German and English. Earlier versions of Sibylle have been used since 2001 in a rehabilitation center (Kerpape, France).

In the past decade various semantic relatedness, similarity, and distance measures have been proposed which play a crucial role in many NLP-applications. Researchers compete for better algorithms (and resources to base the algorithms on), and often only few percentage points seem to suffice in order to prove a new measure (or resource) more accurate than an older one. However, it is still unclear which of them performs best under what conditions. In this work we therefore present a study comparing various relatedness measures. We evaluate them on the basis of a human judgment experiment and also examine several practical issues, such as run time and coverage. We show that the performance of all measures -as compared to human estimates -is still mediocre and argue that the definition of a shared task might bring us considerably closer to results of high quality.

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In this paper, we describe the latest version of SIBYLLE, an AAC system that permits persons suffering from severe physical disabilities to enter text with any computer application and also to compose messages to be read out by a speech synthesis module. The system consists of a virtual keyboard comprising a set of keypads which allow entering characters or full words by a single-switch selection process. It also comprises a sophisticated word prediction component which dynamically calculates the most appropriate words for a given context. This component is auto-adaptive, i.e. it learns on every text the user has entered. It thus adapts its predictions to the user's language and the current topic of communication as well. So far the system works for French, German and English. Earlier versions of SIBYLLE have been used since 2001 in the Kerpape 1 rehabilitation center (Brittany, France).

No abstract