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The search for robustness in natural language understanding

The search for robustness in natural language understanding Practical natural language understanding systems used to be concerned with very small miniature domains only: They knew exactly what potential text might be about, and what kind of sentence structures to expect. This optimistic assumption is no longer feasible if NLU is to scale up to deal with text that naturally occurs in the "real world". The key issue is robustness: The system needs to be prepared for cases where the input data does not correspond to the expectations encoded in the grammar. In this paper, we survey the approaches towards the robustness problem that have been developed throughout the last decade. We inspect techniques to overcome both syntactically and semantically ill-formed input in sentence parsing and then look briefly into more recent ideas concerning the extraction of information from texts, and the related question of the role that linguistic research plays in this game. Finally, the robust sentence parsing schemes are classified on a more abstract level of analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

The search for robustness in natural language understanding

Artificial Intelligence Review , Volume 6 (4) – May 24, 2004

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References (59)

Publisher
Springer Journals
Copyright
Copyright
Subject
Computer Science; Artificial Intelligence; Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/BF00123691
Publisher site
See Article on Publisher Site

Abstract

Practical natural language understanding systems used to be concerned with very small miniature domains only: They knew exactly what potential text might be about, and what kind of sentence structures to expect. This optimistic assumption is no longer feasible if NLU is to scale up to deal with text that naturally occurs in the "real world". The key issue is robustness: The system needs to be prepared for cases where the input data does not correspond to the expectations encoded in the grammar. In this paper, we survey the approaches towards the robustness problem that have been developed throughout the last decade. We inspect techniques to overcome both syntactically and semantically ill-formed input in sentence parsing and then look briefly into more recent ideas concerning the extraction of information from texts, and the related question of the role that linguistic research plays in this game. Finally, the robust sentence parsing schemes are classified on a more abstract level of analysis.

Journal

Artificial Intelligence ReviewSpringer Journals

Published: May 24, 2004

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