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...

. Previous approaches to robustness in natural language processing usually treat deviant input by relaxing grammatical constraints whenever a successful analysis cannot be provided by "normal" means. This schema implies, that error detection always comes prior to error handling, a behaviour which hardly can compete with its human model, where many erroneous situations are treated without even noticing them. The paper analyses the necessary preconditions for achieving a higher degree of robustness in natural language processing and suggests a quite different approach based on a procedure for structural disambiguation. It not only offers the possibility to cope with robustness issues in a more natural way but eventually might be suited to accommodate quite different aspects of robust behaviour within a single framework. 1 Robustness in Natural Language Processing The notion of robustness in natural language processing is a rather broad one and lacks a precise definition. Usually, it is ...

In this paper we propose a new approach to integrating a parser into a statistical speech recognizer. The method is able to incrementally apply grammatical restrictions and robustly combine them with the statistical acoustic and language models. On spontaneous speech data a 11.6% reduction in word error rate could be achieved compared to the baseline system applying statistical models only.

We present an active chart parser which parses left connected wordgraphs in a strictly time synchronous way. The parser performs a beam search on the possible paths through the word graph and on the possible derivations of the unification grammar simultaneously. A metric is given to assign scores to edges, taking into account the whole left context thereby combining acoustic probabilities, n-gram probabilities and unification grammar probabilities. A specialized model for the derivation of typed unification grammars is introduced. Different ways of coupling the parser with an LR beam decoder in an online time synchronous fashion are defined and several experimental results are presented. Two top down and one bottom up method are investigated. In bottom up mode, the decoder sends word hypotheses as they are found from left to right, while the parser keeps step. In verify mode, the decoder is always a frame ahead, while the parser verifies received hypotheses, providing language informat...

This paper revisits word lattice search whose task is to find a plausible semantic interpretation for a given utterance. Our approach of interleaved search and analysis is designed to break the frontier of "toy" applications. The framework is implemented in two interacting modules, running in parallel. Instead of simply parsing a word lattice, we rather do tree decoding with a probabilistic approximation of a given grammar, employing a beam search strategy. Logical form is build up in tandem according to the decoded derivation histories, using a codescriptive HPSG grammar for dialog turns. The proposed architecture only uses the knowledge necessary in every processing step, the key aspect being an asynchronous coupling of the two specialized modules.

. This article describes a probabilistic context free grammar approximation method for unification grammars. In order to produce good results, the method is combined with an N best parsing extension to chart parsing. The first part of the paper introduces the grammar approximation method, while the second part describes details of an efficient N-best packing and unpacking scheme for chart parsing. 1 Introduction Recently much attention has been payed to the integration of speech and language technology 1 . The concentration on spontaneous speech understanding led to the definition of a robust interface known as the word graph or word lattice between recognition and understanding. Depending on the application, systems are built to provide a shallow stochastic analysis or a deep linguistic analysis of the word lattice. Using a shallow stochastic approach, a rough template-based analysis can be achieved which makes sense in those cases where a fine grained reconstruction of meanings is...

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 ...

Abstract. In this paper we describe a screening approach forspeech/ language analysis using learned, at connectionist representations. For investigating this approach we built a hybrid connectionist system using a large number of connectionist and symbolic modules. Our system SCREEN 1 learns a at syntactic and semantic analysis of incremental streams of word hypothesis sequences. In this paper we focus on techniques for improving the quality of pruned hypotheses from a speech recognizer using acoustic, syntactic, and semantic knowledge. We show that the developed architecture is able to cope with real-world spontaneously spoken language in an incremental and parallel manner. 1

The results of an experiment are presented in which an approach for robust parsing has been applied incrementally. They conrm that due to the robust nature of the underlying technology an arbitrary prex of a sentence can be analysed into an intermediate structural description which is able to direct the further analysis with a high degree of reliability. Most notably, this result can be achieved without any special adaptation of the grammar or of the parsing algorithms to the case of incremental processing. The resulting incremental parsing procedure is signicantly faster if compared to a nonincremental best-rst search. Additionally it turns out that longer sentences benet most from this acceleration. 1 Introduction Natural language utterances usually unfold over time, i e. both listening and reading are carried out in an incremental left-to-right manner. Modeling a similar type of behaviour in computer-based solutions is a challenging and particularly interesting task for a numb...

The results of an experiment are presented in which an approach for robust parsing has been applied inerementally. They confirm that due to the robust nature of the underlying technology an arbitrary prefix of a sentence can be analysed into an intermediate structural description which is able to direct the further analysis with a high degree of reliability. Most notably, this result can be achieved without adapting the grammar or the parsing algorithms to the case of incremental processing. The resulting incremental parsing procedure is significantly faster if compared to a non-incremental best-first search. Additionally it turns out that longer sentences benefit most from this acceleration.