A Semantic Expert Using an Online Standard Dictionary

Abstract

A system has been developed to find the most likel y attachment for prepositional phrases in English sentences in a fairly unrestricted way. The system receives as input a syntactic sentence parse provided by a general-purpose computational grammar called PEG (PLNLP English Grammar). The semantic decision that is necessary to make the right attachments is made (a) by parsing (also with PEG) the natural language definitions of an online standard dictionary, in this case Webster's Seventh New Collegiate Dictionary; (b) by relating words to other words in the dictionary; and (c) by reasoning heuristically about the comparative likelihood of different possible attachments. ' The basic assumption of this research is that natural language itself is a knowledge representation language that can be conveniently accessed and richly exploited. Techniques such as those presented here offer hope for eliminating the timeconsuming and often incomplete band coding of semantic information that has been conventional in natural language understanding systems.