This paper describes the details of the system, and includes relevant measurements of size, efficiency, and performance of each of its components

m we can carve o# next. `Partial parsing' is a cover term for a range of di#erent techniques for recovering some but not all of the information contained in a traditional syntactic analysis. Partial parsing techniques, like tagging techniques, aim for reliability and robustness in the face of the vagaries of natural text, by sacrificing completeness of analysis and accepting a low but non-zero error rate. 1 Tagging The earliest taggers [35, 51] had large sets of hand-constructed rules for assigning tags on the basis of words' character patterns and on the basis of the tags assigned to preceding or following words, but they had only small lexica, primarily for exceptions to the rules. TAGGIT [35] was used to generate an initial tagging of the Brown corpus, which was then hand-edited. (Thus it provided the data that has since been used to train other taggers [20].) The tagger described by Garside [56, 34], CLAWS, was a probabilistic version of TAGGIT, and the DeRose tagger improved on

The last decade has witnessed the emergence of a new breed of human computer interfaces that combines several human language technologies to enable information access and transactional processing using spoken dialogue. In this paper, I discuss my view on the research issues involved in the development of such interfaces, describe the recent work done in this area at the MIT Laboratory for Computer Science, and outline some of the unmet research challenges, including the need to work in real domains, spoken language generation, and portability across domains and languages.

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 describes GLR*, a parser that can parse any input sentence by ignoring unrecognizable parts of the sentence. Using an efficient algorithm, the parser is capable of finding and parsing a maximal subset of the original input that is parsable, and therefore return the parse with fewest skipped words. The parser returns some parse(s) for any input sentence, unless no part of the sentence can be recognized at all. Formally, the problem can be defined in the following way: Given a context-free grammar G and a sentence S, find and parse S 0 - the largest subset of words of S, such that S 0 2 L(G). The algorithm described in this chapter is a modification of the Generalized LR (Tomita) parsing algorithm (Tomita, (1986)). The parser accommodates the skipping of words by allowing shift operations to be performed from inactive state nodes of the Graph Structured Stack. A heuristic similar to beam search makes the algorithm computationally tractable. The modified parser, GLR*, h...

We describe the design and performance of a complete spoken language understanding system currently under development at BBN. The system, dubbed HARC (Hear And Respond to Con-tinuous speech), successfully integrates state-of-the-art speech recognition and natural language understanding subsystems. The system has been tested extensively on a restricted airline travel in-formation (ATIS) domain with a vocabulary of about 2000 words. HARC is implemented in portable, high-level software that runs in real time on today's workstations to support interactive online human-machme dialogs. No special purpose hardware is required other than an A/D converter to digitize the speech. The system works well for any native speaker of American English and does not require any enrollment data from the users. We present results of formal DARPA tests in Feb. '92 and Nov. '92.

In this paper we describe a recent experiment designed to evaluate the performance of the TRAINS-95 system. The evaluation uses a task-based evaluation methodology appropriate for dialogue systems such as TRAINS-95, where a human and a computer interact and collaborate to solve a given problem. In task-based evaluations, techniques are measured in terms of their affect on task performance measures such as how long it takes to develop a solution using the system, and the quality of the final plan produced. The evaluation explores the robustness of the TRAINS-95 system in the presence of word recognition errors, the amount of training required to effectively use the system, and user preferences. This work was supported in part by ONR/ARPA grants N00014-92-J-1512 and N00014-95-1-1088, and NSF grant IRI-9503312. 1 Introduction TRAINS-95 is the first end-to-end implementation in a long-term effort to develop an intelligent planning assistant that is conversationally proficient in natural...

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 ...