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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