In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given grammatical module. We decompose the learning problem and present formal results for a central subproblem, deducing the constraint ranking particular to a target language, given structural descriptions of positive examples. The structure imposed on the space of possible grammars by Optimality Theory allows efficient convergence to a correct grammar. We discuss implications for learning from overt data only, as well as other learning issues. We argue that Optimality Theory promotes confluence of the demands of more effective learnability and deeper linguistic explanation.

We present a system for generating parsers based directly on the metaphor of parsing as deduction. Parsing algorithms can be represented directly as deduction systems, and a single deduction engine can interpret such deduction systems so as to implement the corresponding parser. The method generalizes easily to parsers for augmented phrase structure formalisms, such as definiteclause grammars and other logic grammar formalisms, and has been used for rapid prototyping of parsing algorithms for a variety of formalisms including variants of tree-adjoining grammars, categorial grammars, and lexicalized context-free grammars.

In order to realize their full potential, multimodal systems need to support not just input from multiple modes, but also synchronized integration of modes. Johnston et al (1997) model this integration using a unification operation over typed feature structures. This is an effective solution for a broad class of systems, but limits multimodal utterances to combinations of a single spoken phrase with a single gesture. We show how the unification-based approach can be scaled up to provide a full multimodal grammar formalism. In conjunction with a multidimensional chart parser, this approach supports integration of multiple elements distributed across the spatial, temporal, and acoustic dimensions of multimodal interaction. Integration strategies are stated in a high level unification-based rule formalism supporting rapid prototyping and iterative development of multimodal systems. 1 Introduction Multimodal interfaces enable more natural and efficient interaction between humans and mach...

Best-first probabilistic chart parsing attempts to parse efficiently by working on edges that are judged 'best' by some probabilistic figure of merit (FOM). Recent work has used proba- bilistic context-free grammars (PCFGs) to sign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to us- ing a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much finergrained control over parsing effort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results -- that is, our parser achieves equivalent results using one twentieth the number of edges. Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing.

We introduce an abstract form of interobject communication for object-oriented concurrent programming based on the proof theory of Linear Logic, a logic introduced to provide a theoretical basis for the study of concurrency. Such a form of communication, which we call forum-based communication, can be seen as a refinement of blackboard-based communication in terms of a more local notion of resource consumption. Forumbased communication is introduced as part of a new computational model for the object-oriented concurrent programming language LO, presented at last year OOPSLA/ECOOP (1990), which exploits the proof-theory of Linear Logic also to achieve a powerful form of knowledge-sharing.

While symbolic parsers can be viewed as deduction systems, this view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra’s algorithm can be used to construct a probabilistic chart parser with an Ç Ò time bound for arbitrary PCFGs, while preserving as much of the flexibility of symbolic chart parsers as allowed by the inherent ordering of probabilistic dependencies. 1

The analysis of spoken language is widely considered to be a more challenging task than the analysis of written text. All of the difficulties of written language can generally be found in spoken language as well. Parsing spontaneous speech must, however, also deal with problems such as speech disfluencies, the looser notion of grammaticality, and the lack of clearly marked sentence boundaries. The contamination of the input with errors of a speech recognizer can further exacerbate these problems. Most natural language parsing algorithms are designed to analyze "clean" grammatical input. Because they reject any input which is found to be ungrammatical in even the slightest way, such parsers are unsuitable for parsing spontaneous speech, where completely grammatical input is the exception more than the rule. This thesis describes GLR*, a parsing system based on Tomita's Generalized LR parsing algorithm, that was designed to be robust to two particular types of extra-grammaticality: noise...

This chapter introduces weighted bilexical grammars, a formalism in which individual lexical items, such as verbs and their arguments, can have idiosyncratic selectional influences on each other. Such ‘bilexicalism ’ has been a theme of much current work in parsing. The new formalism can be used to describe bilexical approaches to both dependency and phrase-structure grammars, and a slight modification yields link grammars. Its scoring approach is compatible with a wide variety of probability models. The obvious parsing algorithm for bilexical grammars (used by most previous authors) takes time O(n^5). A more efficient O(n³) method is exhibited. The new algorithm has been implemented and used in a large parsing experiment (Eisner, 1996b). We also give a useful extension to the case where the parser must undo a stochastic transduction that has altered the input.

This paper investigates the role of resource allocation as a source of processing difficulty in human sentence comprehension. The paper proposes a simple informationtheoretic characterization of processing difficulty as the work incurred by resource reallocation during parallel, incremental, probabilistic disambiguation in sentence comprehension, and demonstrates its equivalence to the theory of Hale (2001), in which the difficulty of a word is proportional to its surprisal (its negative log-probability) in the context within which it appears. This proposal subsumes and clarifies findings that high-constraint contexts can facilitate lexical processing, and connects these findings to well-known models of parallel constraint-based comprehension. In addition, the theory leads to a number of specific predictions about the role of expectation in syntactic comprehension, including the reversal of locality-based difficulty patterns in syntactically constrained contexts, and conditions under which increased ambiguity facilitates processing. The paper examines a range of established results bearing on these predictions, and shows that they are largely consistent with the surprisal theory.

This paper describes a structure-sharing method for the representation of complex phrase types in a parser for PATR-II, a unification-based grammar formalism. In parsers for unification-based...