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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. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis.
Metadaten
Author:Berthold CrysmannORCiDGND, Anette Frank, Bernd Kiefer, Stefan Müller, Günter Neumann, Jakub Piskorski, Ulrich Schäfer, Melanie SiegelORCiDGND, Hans Uszkoreit, Feiyu Xu, Markus Becker, Hans-Ulrich Krieger
URN:urn:nbn:de:hebis:30:3-236622
DOI:https://doi.org/10.3115/1073083.1073157
Parent Title (English):40th annual meeting of the Association for Computational Linguistics : proceedings of the conference
Publisher:University of Pennsylvania
Place of publication:Philadelphia
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2011/12/21
Year of first Publication:2002
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Contributing Corporation:Association for Computational Linguistics
Release Date:2011/12/21
Tag:Computerlinguistik
Generic NLP Architecture; HPSG Parsing; IE; Shallow NLP; XML
Issue:Art. P0270
Page Number:8
First Page:441
Last Page:448
HeBIS-PPN:424198762
Institutes:Extern
Dewey Decimal Classification:4 Sprache / 41 Linguistik / 410 Linguistik
Sammlungen:Linguistik
Linguistik-Klassifikation:Linguistik-Klassifikation: Computerlinguistik / Computational linguistics
Licence (German):License LogoDeutsches Urheberrecht