Compose-Reduce Parsing

Two new parsing algorithms for context-free phrase structure grammars are presented which perform a bounded amount of processing per word per analysis path, independently of sentence length. They are thus capable of parsing in real-time in a parallel implementation which forks processors in response to non-determinis-tic choice points. 0. INTRODUCTION The work reported here grew out of our attempt to improve on the o (n 2) performance of the SIMD parallel parser described in (Thompson 1991). Rather than start with a commitment to a specific SIMD architecture, as that work had, we agreed that the best place to start was with a more abstract architecture-independent consideration of the CF-PSG parsing problem-given arbitrary resources, what algorithms could one envisage which could recognise and/or parse atomic category phrase-structure grammars in o (n) ? In the end, two quite different approaches emerged. One took as its starting point non-deterministic shift-reduce parsing, and sought to achieve linear (indeed real-time) complexity by performing a constant-time step per word of the input. The other took as its starting point tabular parsing (Earley, C KY), and sought to achieve linear complexity by performing a constant-time step for the identi-fication/construction of constituents of each length from 0 to n. The latter route has been widely canvassed, although to our knowledge has not yet been implemented-see (Nijholt 1989, 90) for extensive references. The former route, whereby real-time parsing is achieved by processor forking at non-deterministic choice points in an extended shill-reduce parser, is to our knowledge new. In this paper we present outlines of two such parsers, which we call compose-reduce parsers. L COMPOSE-Rk~nUCE PARSING Why couldn't a simple breadth-first chart parser achieve linear performance on an appropriate parallel system? If you provided enough processors to immediately process all agenda entries as they were created, would not this give the desired result? No, because the processing of a single word might require many serialised 87