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...

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.

We present an approach to the parsing of dependency structures which. brings together the notion of parsing as candidate elimination, the use of graded constraints, and the parallel disambiguation of related structural representations. The approach aims at an increased level of robustness by accepting constraint violations in a controlled way, combining redundant and possibly conflicting information on different represenrational levels, and facilitating partial parsing as a natural mode of behavior.

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...

This paper investigates the impact of Constraint Dependency Grammars (CDG) on the accuracy of an integrated speech recognition and CDG parsing system. We compare a conventional CDG with CDGs that are induced from annotated sentences and template-expanded sentences. The grammars are evaluated on parsing speed, precision/coverage, and improvement of word and sentence accuracy of the integrated system. Sentence-derived CDGs significantly improve recognition accuracy over the conventional CDG but are less general. Expanding the sentences with templates provides us with a mechanism for increasing the coverage of the grammar with only minor reductions in recognition accuracy.

The focus of this thesis proposal is to improve the ability of a computational system to understand spoken utterances in a dialogue with a human. Available computational methods for word recognition do not perform as well on spontaneous speech as we would hope. Even a state of the art recognizer achieves slightly worse than 70% word accuracy on (nearly) spontaneous speech in a conversation about a specific problem. To address this problem, I will explore novel methods for post-processing the output of a speech recognizer in order to correct errors. I adopt statistical techniques for modeling the noisy channel from the speaker to the listener in order to correct some of the errors introduced there. The statistical model accounts for frequent errors such as simple word/word confusions and short phrasal problems (one-to-many word substitutionsand many-to-one word concatenations). To use the model, a search algorithm is required to find the most likely correction of a given word sequence ...

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] ...

This paper describes the implementation of a constraint-based parser, PARSEC (Parallel ARchitecture SEntence Constrainer), which has the required flexibility that a user may easily construct a custom grammar and test it. Once the user designs grammar parameters, constraints, and a lexicon, our system checks them for consistency and creates a parser for the grammar. The parser has an X-windows interface that allows a user to view the state of a parse of a sentence, test new constraints, and dump the constraint network to a file. The parser has an option to perform the computationally expensive constraint propagation steps on the MasPar MP-1. Stream and socket communication was used to interface the MasPar constraint parser with a standard X-windows interface on our Sun Sparcstation

Natural language processing has been in existence for more than fifty years. During this time, it has significantly contributed to the field of human-computer interaction in terms of theoretical results and practical applications. As computers continue to become more affordable and accessible, the importance of user interfaces that are effective, robust, unobtrusive, and user-friendly -- regardless of user expertise or impediments -- becomes more pronounced. Since natural language usually provides for effortless and effective communication in human-human interaction, its significance and potential in human-computer interaction should not be overlooked -- either spoken or typewritten, it may effectively complement other available modalities, such as windows, icons, and menus, and pointing; in some cases, such as in users with disabilities, natural language may even be the only applicable modality. This chapter examines the field of natural language processing as it relates to humanc...