This paper describes an efficient and robust implementation of a bidirectional, head-driven parser for constraint-based grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport can be obtained by telephone. After a Review

This paper describes a bidirectional head-corner parser for (unification-based versions of) Lexicalized Tree Adjoining Grammars. Keywords: head-driven parsing, bidirectional parsing, Tree Adjoining Grammar. 1 Introduction Natural language parsing is still inefficient. There are two important reasons for this. Natural language contains many ambiguities which lead to a large search space for parsers. On the other hand the complex structure of natural language is problematic; natural language parsers usually are based on grammars of which the formal power goes beyond the context-free grammars. Both observations imply that efficient algorithms that have been developed for e.g. programming languages, such as LR(1), can not be used as such. To obtain a parsing algorithm for a class of grammars beyond the context-free grammars, the usual strategy is to generalize an efficient algorithm for context-free grammars to this more powerful class. The problem with this approach is that in the course...

In this paper a bidirectional parser for Lexicalized Tree Adjoining Grammars will be presented. The algorithm takes advantage of a peculiar characteristic of Lexicalized TAGs, i.e. that each elementary tree is associated with a lexical item, called its anchor. The algorithm employs a mixed strategy: it works bottom -up from the lexical anchors and then expands (.partial) analyses making top-down predictions. Even if such an algorithm does not improve the worst-case time bounds of already known TAGs parsing methods, it could be relevant from the perspective of linguistic information processing, because it employs lexical information in a more direct way.

We show that more head-driven parsing algorithms can be formulated than those occurring in the existing literature. These algorithms are inspired by a family of left-to-right parsing algorithms from a recent publica- tion. We further introduce a more advanced notion of "head-driven parsing" which allows more detailed specification of the processing order of non-head elements in the right-hand side. We develop a parsing algorithm for this strategy, based on LR parsing techniques.

Parsing schemata provide a high-level formal description of parsers. These can be used, among others, as an intermediate level of abstraction for deriving the formal correctness of a parser. A parser is correct if it duely implements a parsing schema that is known to be correct. In this paper it is discussed how the correctness of a parsing schema can be proven and how parsing schemata relate to some well-known classes of parsers, viz. chart parsers and LR-type parsers.

Abstract not found

Parsing schemata are defined as an intermediate level of abstraction between contextfree grammars and parsers. Clear, concise specifications of radically different parsing algorithms can be expressed as parsing schemata. Moreover, because of the uniformity of these specifications, relations between different parsing algorithms can be formally established. This article gives an introduction to the parsing schemata framework. 1 Introduction A wide variety of parsing algorithms can be found in the Computer Science and Computational Linguistics literature. Algorithms differ a lot with respect to languages in which they are expressed, data structures used, degree of formality, class of grammars that can be handled, etc. Things get worse when we consider parallel, rather than sequential algorithms, because many different architectures can be exploited. One can compare parsers by evaluating run-time performance, but in order to get a deeper insight into the relative merits and deficiencies o...

Head-Corner (HC) parsing has come up in computational linguistics a few years ago, motivated by linguistic arguments. This idea is a heuristic, rather than a fail-safe principle, hence it is relevant indeed to consider the worst-case behaviour of the HC parser. We define a novel predictive head-corner chart parser of cubic time complexity. We start with a left-corner (LC) chart parser, which is easier to understand. Subsequently, the LC chart parser is generalized to an HC chart parser. It is briefly sketched how the parser can be enhanced with feature structures. 1. Introduction "Our Latin teachers were apparently right", Martin Kay (1989) remarks. "You should start [parsing] with the main verb. This will tell you what kinds of subjects and objects to look for and what cases they will be in. When you come to look for these, you should also start by trying to find the main word, because this will tell you most about what else to look for". Head-driven or head-corner parsing has been ...

ion (Johnson and Dorre, [39]) ffl x(A,B,f(A,B),g(A,h(B,i(C)))) =) x(A,B,f(,),g(,)) ffl parsewithweakening(Cat,P0,P,E0,E) :- weaken(Cat,WeakenedCat), parse(WeakenedCat,P0,P,E0,E), Cat=WeakenedCat. ffl Really helps! Ambiguity Packing ffl A parser should not construct all parse trees (exponential) ffl Instead, a compact representation of all such parse trees are constructed -- grammar [42, 9] -- parse forest [76] -- packed structures [3] ffl Here: for every `result item' keep track of the lexical entry and references of other result items that were used to create it ffl Results in a lexicalized tree substitution grammar ffl which generates the input sentence with all its parse trees Bottom-up Inactive-chart Parser Item form: [i;X; j] Axioms: Goals: [0;S;n] Inference Rules: Scan [q i ;wi; qi+1 ] Complete [q k ;X1; q k 0][q k 0;X2; q k 00] : : : [q m0;Xl; qm] [q k ;X0; qm] X0 !X1:::Xl Bottom-up Inactive-chart Parser Inference Rules: Scan [q i ;wi; qi+...