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

Multiple Context-Free Tree Grammars: Lexicalization and Characterization

Multiple (simple) context-free tree grammars are investigated, where "simple" means "linear and nondeleting". Every multiple context-free tree grammar that is finitely ambiguous can be lexicalized; i.e., it can be transformed into an equivalent one (generating the same tree language) in which each rule of the grammar contains a lexical symbol. Due to this transformation, the rank of the nonterminals increases at most by 1, and the multiplicity (or fan-out) of the grammar increases at most by the maximal rank of the lexical symbols; in particular, the multiplicity does not increase when all lexical symbols have rank 0. Multiple context-free tree grammars have the same tree generating power as multi-component tree adjoining grammars (provided the latter can use a root-marker). Moreover, every multi-component tree adjoining grammar that is finitely ambiguous can be lexicalized. Multiple context-free tree grammars have the same string generating power as multiple context-free (string) grammars and polynomial time parsing algorithms. A tree language can be generated by a multiple context-free tree grammar if and only if it is the image of a regular tree language under a deterministic finite-copying macro tree transducer. Multiple context-free tree grammars can be used as a synchronous translation device.Comment: 78 pages, 13 figure

Producing power-law distributions and damping word frequencies with two-stage language models

Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statisticalmodels that can generically produce power laws, breaking generativemodels into two stages. The first stage, the generator, can be any standard probabilistic model, while the second stage, the adaptor, transforms the word frequencies of this model to provide a closer match to natural language. We show that two commonly used Bayesian models, the Dirichlet-multinomial model and the Dirichlet process, can be viewed as special cases of our framework. We discuss two stochastic processes-the Chinese restaurant process and its two-parameter generalization based on the Pitman-Yor process-that can be used as adaptors in our framework to produce power-law distributions over word frequencies. We show that these adaptors justify common estimation procedures based on logarithmic or inverse-power transformations of empirical frequencies. In addition, taking the Pitman-Yor Chinese restaurant process as an adaptor justifies the appearance of type frequencies in formal analyses of natural language and improves the performance of a model for unsupervised learning of morphology.48 page(s

Practical experiments with regular approximation of context-free languages

Several methods are discussed that construct a finite automaton given a context-free grammar, including both methods that lead to subsets and those that lead to supersets of the original context-free language. Some of these methods of regular approximation are new, and some others are presented here in a more refined form with respect to existing literature. Practical experiments with the different methods of regular approximation are performed for spoken-language input: hypotheses from a speech recognizer are filtered through a finite automaton.Comment: 28 pages. To appear in Computational Linguistics 26(1), March 200

Parsing Strategies With \u27Lexicalized\u27 Grammars: Application to Tree Adjoining Grammars

In this paper, we present a parsing strategy that arose from the development of an Earley-type parsing algorithm for TAGs (Schabes and Joshi 1988) and from some recent linguistic work in TAGs (Abeillé: 1988a). In our approach, each elementary structure is systematically associated with a lexical head. These structures specify extended domains of locality (as compared to a context-free grammar) over which constraints can be stated. These constraints either hold within the elementary structure itself or specify what other structures can be composed with a given elementary structure. The \u27grammar\u27 consists of a lexicon where each lexical item is associated with a finite number of structures for which that item is the head. There are no separate grammar rules. There are, of course, \u27rules\u27 which tell us how these structures are composed. A grammar of this form will be said to be \u27lexicalized\u27. We show that in general context-free grammars cannot be \u27lexicalized\u27. We then show how a \u27lexicalized\u27 grammar naturally follows from the extended domain of locality of TAGs and examine briefly some of the linguistic implications of our approach. A general parsing strategy for \u27lexicalized\u27 grammars is discussed. In the first stage, the parser selects a set of elementary structures associated with the lexical items in the input sentence, and in the second stage the sentence is parsed with respect to this set. The strategy is independent of nature of the elementary structures in the underlying grammar. However, we focus our attention on TAGs. Since the set of trees selected at the end of the first stage is not infinite, the parser can use in principle any search strategy. Thus, in particular, a top-down strategy can be used since problems due to recursive structures are eliminated. We then explain how the Earley-type parser for TAGs can be modified to take advantage of this approach

Tree-Adjoining Grammars and Lexicalized Grammars

In this paper, we will describe a tree generating system called tree-adjoining grammar(TAG)and state some of the recent results about TAGs. The work on TAGS is motivated by linguistic considerations. However, a number of formal results have been established for TAGs, which we believe, would be of interest to researchers in tree grammars and tree automata. After giving a short introduction to TAG, we briefly state these results concerning both the properties of the string sets and tree sets (Section 2). We will also describe the notion of lexicalization of grammars (Section 3) and investigate the relationship of lexicalization to context-free grammars (CFGs) and TAGS (Section 4)