The VERBMOBIL domain model version 1.0

This report describes the domain model used in the German Machine Translation project VERBMOBIL. In order make the design principles underlying the modeling explicit, we begin with a brief sketch of the VERBMOBIL demonstrator architecture from the perspective of the domain model. We then present some rather general considerations on the nature of domain modeling and its relationship to semantics. We claim that the semantic information contained in the model mainly serves two tasks. For one thing, it provides the basis for a conceptual transfer from German to English; on the other hand, it provides information needed for disambiguation. We argue that these tasks pose different requirements, and that domain modeling in general is highly task-dependent. A brief overview of domain models or ontologies used in existing NLP systems confirms this position. We finally describe the different parts of the domain model, explain our design decisions, and present examples of how the information contained in the model can be actually used in the VERBMOBIL demonstrator. In doing so, we also point out the main functionality of FLEX, the Description Logic system used for the modeling

Applying DL in Automatic Dialogue Interpreting

this paper I present the application of the FLE

Description Logic Unplugged

this paper we fill this gap and adopt the approach of [1] to DL. We only describe the task of representing scope ambiguity here; further investigation is necessary for disambiguation. In general, it would be possible to introduce holes in the TBox as well, resulting in an underspecified terminology. But for now we restrict occurrences of holes to the ABox. In contrast to [1], there is no need for labels, because the relevant target entities already have names, namely those of objects. In the next section we present the syntax and semantics of an Unplugged Description Logic (UDL), more precisely a DL with a partly unplugged ABox. Then we present an algorithm of a constraint solver for solving the consistency problem of UDL. Because all interesting inferences can be reduced to consistency, this yields algorithms for subsumption and instance checking

From Human Evaluation to Automatic Selection of Good Translations

In this paper we describe a machine learning method tailored to overcome the difficulty of selecting and putting together translated segments in the Verbmobil system. We use off line human feedback to determine an optimized confidence rescaling scheme for the confidence values provided by four independent and competing translation paths in Verbmobil. 1

Representing and Querying Standoff XML

The paper discusses the representation and exploitation of multi-level annotated linguistic data. We first present a standoff XML representation, which distributes information over separate, standoff layers and allows us to represent annotations of various kinds in a uniform, generic way. This format serves as our interchange format. We further introduce an XML-inline representation that is designed to provide for a more efficient processing of the data. This format is computed on the basis of the standoff representation and uses fragments to represent overlapping elements. We then compare both representations by testing their performance with regard to a testsuite. Not surprisingly, the inline variant performs much better than the standoff variant, in particular with more complex queries