Walter Kasper

DISCO: an HPSG-based NLP system and its application for appointment scheduling

The natural language system DISCO is described. It combines o a powerful and flexible grammar development system; o linguistic competence for German including morphology, syntax and semantics; o new methods for linguistic performance modelling on the basis of high-level competence grammars; o new methods for modelling multi-agent dialogue competence; o an interesting sample application for appointment scheduling and calendar management.

Charting the Depths of Robust Speech Parsing

We describe a novel method for coping with ungrammatical input based on the use of chart-like data structures, which permit anytime processing. Priority is given to deep syntactic analysis. Should this fail, the best partial analyses are selected, according to a shortest-paths algorithm, and assembled in a robust processing phase. The method has been applied in a speech translation project with large HPSG grammars.

A system for the semantic multimodal analysis of news audio-visual content

News-related content is nowadays among the most popular types of content for users in everyday applications. Although the generation and distribution of news content has become commonplace, due to the availability of inexpensive media capturing devices and the development of media sharing services targeting both professional and user-generated news content, the automatic analysis and annotation that is required for supporting intelligent search and delivery of this content remains an open issue. In this paper, a complete architecture for knowledge-assisted multimodal analysis of news-related multimedia content is presented, along with its constituent components. The proposed analysis architecture employs state-of-the-art methods for the analysis of each individual modality (visual, audio, text) separately and proposes a novel fusion technique based on the particular characteristics of news-related content for the combination of the individual modality analysis results. Experimental results on news broadcast video illustrate the usefulness of the proposed techniques in the automatic generation of semantic annotations.

Modularizing codescriptive grammars for efficient parsing

Unification-based theories of grammar allow to integrate different levels of linguistic descriptions in the common framework of typed feature structures. Dependencies among the levels are expressed by coreferences. Though highly attractive theoretically, using such codescriptions for analysis creates problems of efficiency. We present an approach to a modular use of codescriptions on the syntactic and semantic level. Grammatical analysis is performed by tightly coupled parsers running in tandem, each using only designated parts of the grammatical description. In the paper we describe the partitioning of grammatical information for the parsers and present results about the performance.

Deep Linguistic Analysis with HPSG

Deep linguistic analysis is based on Head-Driven Phrase Structure Grammar (HPSG) which provides an integrated approach to syntactic and semantic analysis. We present the basic concepts and ideas of HPSG, as well as of the underlying semantic representation formalism and its interface to the Verbmobil system.

OpenLogos machine translation: philosophy, model, resources and customization

This paper reviews the OpenLogos rule-based machine translation system, and describes its model architecture as an incremental pipeline process. The paper also describes OpenLogos resources and their customization to specific application domains. One of the key aspects of rule-based machine translation systems intelligence is the symbology employed by these systems in representing natural language internally. The paper offers details about the OpenLogos semantico-syntactic abstract representation language known as SAL. The paper also shows how OpenLogos has addressed classic problems of rule-based machine translation, such as the cognitive complexity and ambiguity encountered in natural language processing, illustrating how SAL helps overcome them in ways distinct from other existing rule-based machine translation systems. The paper illustrates how the intelligence inherent in SAL contributes to translation quality, presenting examples of OpenLogos output of a kind that non-linguistic systems would likely have difficulty emulating. The paper shows the unique manner in which OpenLogos applies the rulebase to the input stream and the kind of results produced that are characteristic of the OpenLogos output. Finally, the paper deals with an important advantage of rule-based machine translation systems, namely, the customization and adaption to application-specific needs with respect to their special terminology and transfer requirements. OpenLogos offers users a set of comfortable customization tools that do not require special knowledge of the system internals. An overview of the possibilities that these tools provide will be presented.

Integration of Prosodic and Grammatical Information in the Analysis of Dialogs

The analysis of spoken dialogs requires the analysis of complete multi-sentence turns. Especially, the segmentation of turns in sentential or phrasal segments is a problem. In this paper we present a system for turn analysis. It is based on an extension of HPSG grammar for turns and takes into account extra-linguistic prosodic information. We show how this information can be integrated and represented in the grammar, and how it is used to reduce the search space in parsing.

Distributed parsing with HPSG grammars

Unification-based theories of grammar allow for an integration of different levels of linguistic descriptions in the common framework of typed feature structures. Dependencies among the levels are expressed by coreferences. Though highly attractive theoretically, using such codescriptions for analysis create problems of efficiency. We present an approach to a modular use of codescriptions on the syntactic and semantic level. Grammatical analysis is performed by tightly coupled parsers running in tandem, each using only designated parts of the grammatical description. In the paper we describe the partitioning of grammatical information for the parsers and present results about the performance.

Semi-automatic Generation of Multiple-Choice Tests from Mentions of Semantic Relations

We propose a strategy for the semiautomatic generation of learning material for reading-comprehension tests, guided by semantic relations embedded in expository texts. Our approach combines methods from the areas of information extraction and paraphrasing in order to present a language teacher with a set of candidate multiple-choice questions and answers that can be used for verifying a language learners reading capabilities. We implemented a web-based prototype showing the feasibility of our approach and carried out a pilot user evaluation that resulted in encouraging feedback but also pointed out aspects of the strategy and prototype implementation which need improvements.

Knowledge-assisted cross-media analysis of audio-visual content in the news domain

In this paper, a complete architecture for knowledge-assisted cross-media analysis of News-related multimedia content is presented, along with its constituent components. The proposed analysis architecture employs state-of-the-art methods for the analysis of each individual modality (visual, audio, text) separately, and proposes a fusion technique based on the particular characteristics of News-related content for the combination of the individual modality analysis results. Experimental results on news broadcast video illustrate the usefulness of the proposed techniques in the automatic generation of semantic video annotations.