Hello, The CVS version of Happy now includes an extension for GLR parsing. GLR parsing extends LR parsing to ambiguous grammars, producing a directed, acyclic and-or graph ("a packed forest") to compactly represent the multiple possible parses. There are many possible applications, not least natural language, bioinformatics, sequence alignment, code generation... It comprises an additional back-end and drivers for Happy. The code generated is 100% Haskell-98 or can be optimised for GHC. The latter is sufficiently efficient to parse a gene sequence of length 600 to a graph of some 11000 nodes in about 10 seconds. It has two modes of recording semantic information, either calculating the list of all possible results or allowing detailed labelling of the forest's nodes. It is based on undergraduate work by Ben Medlock (Durham), with some use of ideas from Peter Ljungloef (Chalmers), and subsequently extended / improved by myself. Its unique feature is the production of an explicit graph, which is essential for applications involving heavy ambiguity - where simply processing lists of alternatives leads to explosion of possibilities. A new release of Happy containing this code is planned soon, but if anyone is curious and would like to try the code out in advance, I'd be grateful for comments / bugs reports. You can build from CVS, or download a linux distribution from http://www.dur.ac.uk/p.c.callaghan/glr-dist.tgz This package contains brief instructions and some examples. Executing "make in-place" in the distribution's root directory should fix up paths to allow the examples to be compiled. The brief instructions, with an example of a forest, can be viewed at http://www.dur.ac.uk/p.c.callaghan/glr-simple-docs The forest can also be viewed graphically, see http://www.dur.ac.uk/p.c.callaghan/glr-four.eps Enjoy! Paul Callaghan -------------------------------------------------------------------------------- Dr Paul Callaghan (Lecturer) Email: P.C.Callaghan@durham.ac.uk Computer Assisted Reasoning Group, Department of Computer Science, Durham http://www.dur.ac.uk/CARG