A finite and real-time processor for natural language
Communications of the ACM
An efficient context-free parsing algorithm
Communications of the ACM
Revised report on the algorithm language ALGOL 60
Communications of the ACM
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Theory of Syntactic Recognition for Natural Languages
Theory of Syntactic Recognition for Natural Languages
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Most artificial natural language processing (NLP) systems make use of some simple algorithm for parsing. These algorithms overlook the inextricable link between parsing natural language and understanding it. Humans parse language in a linear fashion. Our goal is to develop an NLP system that parses in a linear and psychologically valid fashion. When this goal is achieved, our NLP system will be efficient, and it will generate the correct interpretation in ambiguous situations. In this paper, we describe two NLP systems, whose parsing is driven by several heuristics. The first is a bottom-up system which is based on the work of (Ford, Bresnan & Kaplan 1982). The second system is a more expansive attempt, incorporating the initial heuristics and several more. This system runs on a much larger domain and incorporates several new syntactic forms. It has its weaknesses, but it shows good progress toward the goal of linearity.