Natural language parsing is conceived to be a procedure of disambiguation, which successively reduces an initially totally ambiguous structural representation towards a single interpretation. Graded constraints are used as means to express wellformedness conditions of different strength and to decide which partial structures are locally least preferred and, hence, can be deleted. This approach facilitates a higher degree of robustness of the analysis, allows to introduce resource adaptivity into the parsing procedure, and exhibits a high potential for parallelization of the computation.

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.

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 the parse for a sentence. At its simplest level, the system can be thought of as a generalpurpose query answering system for multiple topical databases. The user's input would be processed by the language processor which interfaces to the databases with the goal of interacting with the correct database in order to provide a reasonable answer to the user's spoken request. Initially, it analyzes a word graph of sentence hypotheses provided by a speech recognizer using general syntactic and semantic rules. Then, if the utterance is still ambiguous, it utilizes contextspecific constraints to further refine the analysis. This brings us closer to developing a more general purpose interface f...

Parsing of dependency grammar has been modeled as a constraint satisfaction problem. In this paper a restricted kind of constraints is proposed, which is simple enough to be implemented efficiently, but which is also rich enough to express a wide variety of grammatical well-formedness conditions. We give a number of examples to demonstrate how different kinds of linguistic knowledge can be encoded in this formalism.

The question of how to integrate language models with speech recognition systems is becoming more important as speech recognition technology matures. For the purposes of this paper, we have classified the level of integration of current and past approaches into three categories: tightly-coupled, loosely-coupled, or semicoupled systems. We then argue that loose coupling is more appropriate given the current state of the art and given that it allows one to measure more precisely which components of the language model are most important. We will detail how the speech component in our approach interacts with the language model and discuss why we chose our language model. 1 Introduction State of the art speech recognition systems achieve high recognition accuracies only on tasks that have low perplexities. The perplexity of a task is, roughly speaking, the average number of choices at any decision point. The perplexity of a task is at a minimum when the true language model is known and co...

The eliminative nature of Constraint Satisfaction over finite domains offers an interesting potential for robustness in the parsing of spoken language. An approach is presented, which puts unusually ambitious demands on the design of the Constraint Satisfaction procedure by trying to combine preferential reasoning, dynamic scheduling, parallel processing and incremental constraint solving within a coherent solution. 1 Introduction Spoken language processing involves a number of novel requirements as compared to traditional techniques for the treatment of written text. Among those which deserve special attention are: 1. the frequent occurrence of deviant language in particular if utterances are produced spontaneously as part of a problem solving task. Spoken language is not only characterized by hesitations, breaks and restarts, but is additionally marked by different kinds of syntactic and semantic inconsistencies. Most surprisingly, these performance phenomena hardly disturb the la...

We have extended Maruyama's [5, 6, 7] constraint dependency grammar (CDG) to process a lattice or graph of sentence hypotheses instead of separate text strings. A post-processor to a speech recognizer producing N-best hypotheses generates the word graph representation, which is then augmented with information required for parsing. We will summarize the CDG parsing algorithm and then describe how the algorithm is extended to process a word graph on a single processor machine. 1 Introduction The most successful of the current speech recognition systems which process continuous speech for a limited (1000 word) vocabulary are those which utilize hidden Markov models (HMM). Most systems utilizing this approach (e.g., [4, 10])) have reduced recognition errors by incorporating some language information (syntactic and semantic) directly into the HMM to reduce perplexity, but since the goal of these systems is recognition, not understanding, no structural analysis of the utterance is construc...

Constraint Dependency Grammar (CDG) is a constraint-based grammatical formalism which has a weak generative capacity beyond context-free grammars and supports a very flexible parsing algorithm for working with feature grammars; however, the running time of the parser is O(n 4 ). Hence, we have investigated how to improve the running time of the parser by applying feature constraints differentially and by using aggregate unary constraints, which can be applied in O(n 2 ) time. Additional speedup was achieved by integrating the filtering algorithm more tightly with the parser and by supporting the use of a varying number of roles for word classes. Key words: Parsing, NLP, Constraint Satisfaction. Introduction: CDG Parsing Constraint Dependency Grammar, introduced by Maruyama [1, 2, 3], uses constraints to determine which dependencies are grammatical for a sentence. The parsing algorithm is framed as a constraint satisfaction problem: the parsing rules are the constraints and the s...

In this paper, we describe a prototype spoken language system that loosely integrates a speech recognition component based on hidden Markov models with a constraint dependency grammar (CDG) parser using a word graph to pass sentence candidates between the two modules. This loosely coupled system was able to improve the sentence selection accuracy and concept accuracy over the level achieved by the acoustic module with a stochastic grammar. Timing profiles suggest that a tighter coupling of the modules could reduce parsing times of the system, as could the development of better acoustic models and tighter parsing constraints for conjunctions. 1. INTRODUCTION In this paper, we describe a prototype of a spoken language system that integrates a speech recognition component based on hidden Markov models with a constraint dependency grammar (CDG) parser. The underlying goal of our combined system is to identify the 'best' overall sentence candidate with respect to all available knowledge s...

This paper describes the implementation of the PARSEC 1 chip, a vector processing element (PE) for parsing languages. This chip has applications not only in natural language processing, but can also be applied to other constraint satisfaction problems. The PARSEC chip is based on a parsing algorithm which formerly ran in real time on a massively parallel machine [8]; however, the chip can achieve processing speeds fast enough for real-time language processing systems, while at the same time, having a price and form suitable for mass market applications. A key component of any natural language interface is its parsing algorithm. Because some features of English (e.g., context) are clumsy or impossible to handle using existing parsers, we have extended and implemented a parsing algorithm based on a new, flexible grammatical formalism, called Constraint Dependency Grammar (CDG), introduced by Maruyama [11, 12, 13]. Although CDG has proven effective for processing English [6, 20] ...