An assumptive logic programming methodology for parsing

We show how several novel tools in logic programming for AI (namely, continuation based linear and timeless assumptions, and datalog grammars) can assist us in producing terse treatments of difficult language processing phenomena. As a proof of concept, we present a concise parser for datalog grammars (logic grammars where strings are represented with numbered word boundaries rather than as lists of words), that uses assumptions and a combination of left-corner parsing and charting. We then study two test cases of this parser's application: complete constituent coordination, and error diagnosis and correction