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REPRESENTATION AND CONTROL STRATEGIES FOR LARGE KNOWLEDGE DOMAINS: An Application to NLP

 

作者: F. ANTONACCI,   M. RUSSO,   M. T. PAZIENZA,   P. VELARDI,  

 

期刊: Applied Artificial Intelligence  (Taylor Available online 1988)
卷期: Volume 2, issue 3-4  

页码: 213-249

 

ISSN:0883-9514

 

年代: 1988

 

DOI:10.1080/08839518808949909

 

出版商: Taylor & Francis Group

 

数据来源: Taylor

 

摘要:

The design issues encountered during the development of a natural language processor (NLP) for the Italian language are described. The focus is on strategic aspects, namely representation and control, and their implementation with first-order logic. The complexity and the size of the knowledge domain (press agency releases on finance and economics) do not present severe restrictions in the sentence structure; hence a considerable design effort for data structures and control algorithms was required. Logic proved to be an important tool for implementing in a modular and efficient way the knowledge sources along with the programs that derive the morphologic, syntactic, and semantic features of sentences. As for the data structures, we found a considerable advantage in separating linguistic knowledge in three sources: morphologic, syntactic, and semantic. This resulted in a clear and systematic representation scheme and reduced the complexity of the parsing system.

 

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