Efficient Tabular LR Parsing

We give a new treatment of tabular LR parsing, which is an alternative to Tomita's generalized LR algorithm. The advantage is twofold. Firstly, our treatment is conceptually more attractive because it uses simpler concepts, such as grammar transformations and standard tabulation techniques also know as chart parsing. Secondly, the static and dynamic complexity of parsing, both in space and time, is significantly reduced.Comment: 8 pages, uses aclap.st

An Efficient Implementation of the Head-Corner Parser

This paper describes an efficient and robust implementation of a bi-directional, 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 of the motivation for head-driven parsing strategies, and head-corner parsing in particular, a non-deterministic version of the head-corner parser is presented. A memoization technique is applied to obtain a fast parser. A goal-weakening technique is introduced which greatly improves average case efficiency, both in terms of speed and space requirements. I argue in favor of such a memoization strategy with goal-weakening in comparison with ordinary chart-parsers because such a strategy can be applied selectively and therefore enormously reduces the space requirements of the parser, while no practical loss in time-efficiency is observed. On the contrary, experiments are described in which head-corner and left-corner parsers implemented with selective memoization and goal weakening outperform `standard' chart parsers. The experiments include the grammar of the OVIS system and the Alvey NL Tools grammar. Head-corner parsing is a mix of bottom-up and top-down processing. Certain approaches towards robust parsing require purely bottom-up processing. Therefore, it seems that head-corner parsing is unsuitable for such robust parsing techniques. However, it is shown how underspecification (which arises very naturally in a logic programming environment) can be used in the head-corner parser to allow such robust parsing techniques. A particular robust parsing model is described which is implemented in OVIS.Comment: 31 pages, uses cl.st

Interaction Grammars

Interaction Grammar (IG) is a grammatical formalism based on the notion of polarity. Polarities express the resource sensitivity of natural languages by modelling the distinction between saturated and unsaturated syntactic structures. Syntactic composition is represented as a chemical reaction guided by the saturation of polarities. It is expressed in a model-theoretic framework where grammars are constraint systems using the notion of tree description and parsing appears as a process of building tree description models satisfying criteria of saturation and minimality

Natural Language Processing

The subject of Natural Language Processing can be considered in both broad and narrow senses. In the broad sense, it covers processing issues at all levels of natural language understanding, including speech recognition, syntactic and semantic analysis of sentences, reference to the discourse context (including anaphora, inference of referents, and more extended relations of discourse coherence and narrative structure), conversational inference and implicature, and discourse planning and generation. In the narrower sense, it covers the syntactic and semantic processing sentences to deliver semantic objects suitable for referring, inferring, and the like. Of course, the results of inference and reference may under some circumstances play a part in processing in the narrow sense. But the processes that are characteristic of these other modules are not the primary concern

SLR inference: An inference system for fixed-mode logic programs, based on SLR parsing

AbstractDefinite-clause grammars (DCGs) generalize context-free grammars in such a way that Prolog can be used as a parser in the presence of context-sensitive information. Prolog's proof procedure, however, is based on backtracking, which may be a source of inefficiency. Parsers for context-free grammars that use backtracking, for instance, were soon replaced by more efficient methods, such as LR parsers. This suggests incorporating the principles underlying LR parsing into a parser for grammars with context-sensitive information. We present a technique that applies a transformation to the program/grammar by adding leaves to the proof/parse trees and placing the contextual information in such leaves. An inference system is then easily obtained from an LR parser, since only the parts dealing with terminals (which appear at the leaves) must be modified. Although our method is restricted to programs with fixed modes, it may be preferable to DCGs under Prolog for some programs