An evaluation of text analysis technologies
AI Magazine
Building a large-scale knowledge base for machine translation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
An empirical assessment of semantic interpretation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
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The development of natural language processing systems is currently driven to a large extent by measures of knowledge-base size and coverage of individual phenomena relative to a corpus. While these measures have led to significant advances for knowledge-lean applications, they do not adequately motivate progress in computational semantics leading to the development of large-scale, general purpose NLP systems. In this article, we argue that depth of semantic representation is essential for covering a broad range of phenomena in the computational treatment of language and propose depth as an important additional dimension for measuring the semantic coverage of NLP systems. We propose an operationalization of this measure and show how to characterize an NLP system along the dimensions of size, corpus coverage, and depth. The proposed framework is illustrated using several prominent NLP systems. We hope the preliminary proposals made in this article will lead to prolonged debates in the field and will continue to be refined.