Goal oriented parsing

摘要

This paper is devoted to present a new approach to natural language understanding which is called here goal-oriented parsing. The interaction in natural language with artificial systems (robots, data base systems, program generators, question-answering systems, etc.) does not require in most cases of actual interest a full (human like) comprehension of natural language in all its details and nuances. A partial understanding is often enough, wich extracts from the natural language expressions the only significant information which is necessary to construct a correct formal input for the target system. In such a model of comprehension the same meaning is assigned to several different natural language expressions, thus defining a many-to-one mapping between natural language sentences and corresponding formal representations. We argue that a bounded scope, restricted, goal-oriented understanding of natural language may greatly increase the efficency of representation models and parsing algorithms, thus allowing the construction of effective systems. This claim is supported by the design and implementation of a natural language interface to a relational data base called NLI and developed at the Milan Polytechnic Artificial Intelligence Project. In the paper the architecture of the system, the linguistic models, and the parsing algorithms are presented and illustrated through selected examples. Promising directions for future research are outlined as well.