Maximizing Top-down Constraints for Unification-based Systems

A left-corner parsing algorithm with topdown filtering has been reported to show very efficient performance for unificationbased systems. However, due to the nontermination of parsing with left-recursive grammars, top-down constraints must be weakened. In this paper, a general method of maximizing top-down constraints is proposed. The method provides a procedure to dynamically compute *restrictor., a minimum set of features involved in an infinite loop for every propagation path; thus top-down constraints are maximally propagated. 1 I n t r o d u c t i o n A left-corner parsing algorithm with top-down filtering has been reported to show very efficient performance for unification-based systems (Carroll, 1994). In particular, top-down filtering seems to be very effective in increasing parse efficiency (Shann, 1991). Ideally all top-down expectation should be propagated down to the input word so that unsuccessful rule applications are pruned at the earliest time. However, in the context of unification-based parsing, left-recursive grammars have the formal power of a Turing machine, therefore detection of all infinite loops due to left-recursion is impossible (Shieber, 1992). So, top-down constraints must be weakened in order for parsing to be guaranteed to terminate. In order to solve the nontermination problem, Shieber (1985) proposes restrictor, a statically predefined set of features to consider in propagation, and restriction, a filtering function which removes the features not in restrictor from top-down expectation. However, not only does this approach fail to provide a method to automatically generate the restrictor set, it may weaken the predicative power of top-down expectation more than necessary: a globally defined restrictor can only specify the least common features for all propagation paths. In this paper, a general method of maximizing top-down constraints is proposed. The method provides a procedure to dynamically compute *restrictor*, a minimum set of features involved in an infinite loop, for every propagation path. Features in this set are selected by the detection function, and will be ignored in top-down propagation. Using .vestrictor., only the relevant features particular to the propagation path are ignored, thus top-down constraints are maximally propagated.