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Báo cáo khoa học: "An Integrated Architecture for Shallow and Deep Processing"

We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. . | An Integrated Architecture for Shallow and Deep Processing Berthold Crysmann Anette Frank Bernd Kiefer Stefan Miiller Giinter Neumann Jakub Piskorski Ulrich Schafer Melanie Siegel Hans Uszkoreit Feiyu Xu Markus Becker and Hans-Ulrich Krieger DFKI GmbH Stuhlsatzenhausweg 3 Saarbrucken Germany whiteboard@dfki.de Abstract We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular we describe the integration of a high-level HPSG parsing system with different high-performance shallow components ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure called the text chart. We describe details of the integration methods and show how information extraction and language checking applications for real-world German text benefit from a deep grammatical analysis. 1 Introduction Over the last ten years or so the trend in application-oriented natural language processing e.g. in the area of term information and answer extraction has been to argue that for many purposes shallow natural language processing SNLP of texts can provide sufficient information for highly accurate and useful tasks to be carried out. Since the emergence of shallow techniques and the proof of their utility the focus has been to exploit these technolo gies to the maximum often ignoring certain complex issues e.g. those which are typically well handled by deep NLP systems. Up to now deep natural language processing DNLP has not played a significant role in the area of industrial NLP applications since this technology often suffers from insufficient robustness and throughput when confronted with large quantities of unrestricted text. Current information extractions IE .