PARSEC: A Constraint-Based Parser for Spoken Language Processing

PARSEC (1), a text-based and spoken language processing framework based on the Constraint Dependency Grammar (CDG) developed by Maruyama [26,27], is discussed. The scope of CDG is expanded to allow for the analysis of sentences containing lexically ambiguous words, to allow feature analysis in constraints, and to efficiently process multiple sentence candidates that are likely to arise in spoken language processing. The benefits of the CDG parsing approach are summarized. Additionally, the development CDG grammars using PARSEC grammar writing tools and the implementation of the PARSEC parser for word graphs is discussed. (1) Parallel ARchitecture Sentence Constraine

Managing multiple knowledge sources in constraint-based parsing of spoken language

In this paper, we describe a system which is capable of utilizing a variety of knowledge sources to select the most appropriate parse for a spoken sentence. These knowledge sources include syntax, semantics, and contextual information. We discuss one way to utilize contextual information when determining a parse for a sentence. Our definition of a context is defined by which computer application we wish to interact with, where our system is capable of interfacing with two or more applications, each with a fixed vocabulary, syntax, and semanltics. The user is able to interact through a single interface which uses contextual knowledge not only to parse the query, but also to select the appropriate application to interact with. This birings us closer to developing a more general purpose interface for multiple applications

MUSE CSP: An Extension to the Constraint Satisfaction Problem

This paper describes an extension to the constraint satisfaction problem (CSP) approach called MUSE CSP (Multiply SEgmented Constraint Satisfaction Problem). This extension is especially useful for those problems which segment into multiple sets of partially shared variables. Such problems arise naturally in signal processing applications including computer vision, speech processing, and handwriting recognition. For these applications, it is often difficult to segment the data in only one way given the low-level information utilized by the segmentation algorithms. MUSE CSP can be used to efficiently represent and solve several similar instances of the constraint satisfaction problem simultaneously. If multiple instances of a CSP have some common variables which have the same domains and compatible constraints, then they can be combined into a single instance of a MUSE CSP, reducing the work required to enforce node and arc consistency

Extensions to Constraint Dependency Parsing for Spoken Language Processing

A text-based and spoken language processing framework based on the Constraint Dependency Grammar (CDG) developed by Maruyama [24, 25] is discussed. The scope of CDG is expanded to allow for the analysis of sentences containing lexically ambiguous words, to allow feature analysis in constraints, and to efficiently process multiple sentence candidates that are likely to arise in spoken language processing. The benefits of the CDG parsing approach are summarized. Additionally, the development of CDG grammars using our grammar tools and parser is discussed

FASTER MUSE CSP ARC CONSISTENCY ALGORITHMS

MUSE CSP (Multiply SEgmented Constraint Satisfaction Problem) [5, 61 is an extension to the constraint satisfaction problem (CSP) which is especially useful for problems that segment into riultiple instances of CSP that share variables. In Belzerman and Harper [6], the concepts of MUSE node, arc, and path consistency were defined and algorithms for MUSE arc consistency, MUSE AC-1, and MUSE path consistency were developed. MUSE AC-1 is similar to the CSP arc consistency algorithm AC-4 [ l j ] . Recently, Bessikre developed a new algorithm, AC-6 [I], which has the same worst-case running time as AC-4 and is faster than AC-3 and AC-4 in practice. In this paper, we focus on developing two faster MUSE arc consistency algor~thms:M USE AC-2 which directly applies Bessikre\u27s method to improve upon MUSE AC-1, and MUSE AC-3, which uses our new lazy evaluation method for keeping track of the additional sets required by the MUSE approach. These new algorithms decrease the number of steps required to achieve arc ccnsistency in randomly generated MUSE CSP instances when compared to MUSE AC-1. Keyvrords: Problem Solving, Constraint Satisfaction, MUSE arc consistency

An Approach to Multiply Segmented Constraint Satisfaction Problems

This paper describes an extension to the constraint satisfaction problem (CSP) approach called MUSE CSP (MU ltiply SEgmented Constraint Satisfaction Problem). This extension is especially useful for those problems which segment into multiple sets of partially shared variables. Such problems arise naturally in signal processing applications including computer vision, speech processing, and handwriting recognition. For these applications, it is often difficult to segment the data in only one way given the low-level information utilized by the segmentation algorithms. MUSE CSP can be used to efficiently represent several similar instances of the constraint satisfaction problem simultaneously. If multiple instances of a CSP have some common variables which have the same domains and compatible constraints, then they can be combined into a single instance of a MUSE CSP, reducing the work required to enforce node and arc consistency

Faster MUSE CSP Arc Consistency Algorithms

MUSE CSP (MU ltiply SEgmented C onstraint Satisfaction Problem) (Helzerman & Harper, 1994, 1996) is an extension to the constraint satisfaction problem (CSP) which is especially useful for problems which segment into multiple instances of CSP which share variables. MUSE CSP has been used in such diverse applications as improving the accuracy of spoken language understanding systems (Harper & Helzerman, 1995a) and managing contexts in NLP database query systems (Harper & Helzerman, 1995b). In this paper, we focus on providing faster MUSE arc consistency algorithms by applying techniques similar to those developed by Bessière

PAC Learning Constraint Dependency Grammar Constraints

Constraint Dependency Grammar (CDG) [11, 13] is a constraint-based grammatical formalism that has proven effective for processing English [5] and improving the accuracy of spoken language understanding systems [4]. However, prospective users of CDG face a steep learning curve when trying to master this powerful formalism. Therefore, a recent trend in CDG research has been to try to ease the burden of grammar writers by developing methods for automatically learning CDG grammars from annotated sentences [22, 23]. In this paper, we prove that CDG grammar constraints are PAC learnable. 1 Introduction Constraint Dependency Grammar (CDG) [11, 13] is a constraint-based grammatical formalism that has proven effective for processing English [5] and improving the accuracy of spoken language understanding systems [4]. However, prospective users of CDG face a steep learning curve when trying to This material is based upon work supported by a grant from the Intel Research Council and the Nation..