The in-vehicle experience offers a unique challenge for delivering the right amount of information to the driver at the right time. The level of attention required to successfully manage the driving task is often in variable. An ideal in vehicle information delivery system would deliver content to the driver only during low task demand times, such as waiting at a stop light, when the driver's safety would be minimally compromised. The system would also have to respond to sudden changes in the situation such as driver interruption or distraction and terminate gracefully, allowing the driver to refocus on the driving task. In this paper, we present an embedded natural language processing (NLP) system that delivers speech synthesized summarized text content into tailored time slices. The system is also designed to respond dynamically to interruptions. We anticipate that this system could safely deliver speech synthesized content to drivers and allow them to make the most of their time on the road. We have implemented this system on an Atom Z530 processor with 1GB of RAM, a processor comparable to those found in factory installed In-Vehicle Infotainment (IVI) systems and have evaluated it in a laboratory test using a standard NLP corpus to demonstrate this potential.

A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities "treatment" and "disease" in bioscience text, and the problem of identifying such entities. We compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy.

In many types of technical texts, meaning is embedded in noun compounds. A language understanding program needs to be able to interpret these in order to ascertain sentence meaning. We explore the possibility of using an existing lexical hierarchy for the purpose of placing words from a noun compound into categories, and then using this category membership to determine the relation that holds between the nouns. In this paper we present the results of an analysis of this method on two-word noun compounds from the biomedical domain, obtaining classification accuracy of approximately 90%. Since lexical hierarchies are not necessarily ideally suited for this task, we also pose the question: how far down the hierarchy must the algorithm descend before all the terms within the subhierarchy behave uniformly with respect to the semantic relation in question? We find that the topmost levels of the hierarchy yield an accurate classification, thus providing an economic way of assigning relations to noun compounds.

We describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task. The system deals in particularly with detecting when interactions between people occur and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach. We propose and compare two different state-based learning architectures, namely, HMMs and CHMMs for modeling behaviors and interactions. Finally, a synthetic "Alife-style" training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.