In this paper, we investigate the role of physical embodiment of a robot and its degrees of freedom in HRI. Both factors have been suggested to be relevant in definitions of embodiment, and so far we do not understand their effects on the way people interact with robots very well. Linguistic analyses of verbal interactions with robots differing with respect to physical embodiment and degrees of freedom provide a useful methodology to investigate factors conditioning human-robot interaction. Results show that both physical embodiment and degrees of freedom influence interaction, and that the effect of physical embodiment is located in the interpersonal domain, concerning in how far the robot is perceived as an interaction partner, whereas degrees of freedom influence the way users project the suitability of the robot for the current task.

We investigate the utility of supertag information for guiding an existing dependency parser of German. Using weighted constraints to integrate the additionally available information, the decision process of the parser is influenced by changing its preferences, without excluding alternative structural interpretations from being considered. The paper reports on a series of experiments using varying models of supertags that significantly increase the parsing accuracy. In addition, an upper bound on the accuracy that can be achieved with perfect supertags is estimated.

In this paper we investigate the benefit of stochastic predictor components for the parsing quality which can be obtained with a rule-based dependency grammar. By including a chunker, a supertagger, a PP attacher, and a fast probabilistic parser we were able to improve upon the baseline by 3.2%, bringing the overall labelled accuracy to 91.1% on the German NEGRA corpus. We attribute the successful integration to the ability of the underlying grammar model to combine uncertain evidence in a soft manner, thus avoiding the problem of error propagation.

Based on constraint optimization techniques, an architecture for robust parsing of natural language utterances has been developed. The resulting system is able to combine possibly contradicting evidence from a variety of information sources, using a plausibility-based arbitration procedure to derive fairly rich structural representations, comprising aspects of syntax, semantics and other description levels of language. The results of a series of experiments are reported which demonstrate the high potential for robust behaviour with respect to ungrammaticality, incomplete utterances, and temporal pressure.