Large practical NLP applications require robust analysis components that can effectively handle input that is disfluent or extra-grammatical. The effectiveness and efficiency of any robust parser are a direct function of three main factors: (1) Flexibility: what types of disfluencies and deviations from the grammar can the parser handle?; (2) Search: How does the parser search the space of possible interpretations, and what techniques are applied to prune the search space?; and (3) Parse Selection and Disambiguation: What methods and resources are used to evaluate and rank potential parses and sub-parses, and how does the parser cope with the extreme levels of ambiguity introduced by its flexibility parameters? In this chapter we describe our investigations on how to balance flexibility and efficiency in the context of two different robust parsers - a GLR parser and a left corner Chart parser - both based on a unification-augmented context-free grammar formalism. We demonstrate how the...

Language Understanding can be considered as the realization of a mapping from sentences of a natural language into a description of their meaning in an appropriate formal language. Under this viewpoint, the application of the Onward Subsequential Transducer Inference Algorithm (OSTIA) to Language Understanding is considered. The basic version of OSTIA is reviewed and a new version is presented in which syntactic restrictions of the domain and/or range of the target transduction can effectively be taken into account. For experimentation purposes, a task proposed by Feldman et al. for assessing the capabilities of Language Learning and Understanding systems has been adopted and three increasingly difficult-tolearn semantic coding schemes have been defined for this task. In all cases the basic version of OSTIA has consistently proved able to learn very compact and accurate transducers from relatively small training sets of input-output examples of the task. Moreover, if the input sentences are corrupted with syntactic incorrectness or errors, the new version of OSTIA still provides understanding results that only degrade in a gradual and natural way.

To facilitate the development of spoken dialog systems and speech enabled applications, we introduce SGStudio (Semantic Grammar Studio), a grammar authoring tool that enables regular software developers with little speech/linguistic background to rapidly create quality semantic grammars for automatic speech recognition (ASR) and spoken language understanding (SLU). We focus on the underlying technology of SGStudio, including knowledge assisted example-based grammar learning, grammar controls and configurable grammar structures. While the focus of SGStudio is to increase productivity, experimental results show that it also improves the quality of the grammars being developed. Key words: Automatic grammar generation, context free grammars (CFGs), example-based grammar learning, grammar controls, hidden Markov models (HMMs), n-gram model, automatic speech recognition (ASR), spoken language understanding

In this paper, we describe the rationale behind and architecture of a conversational agent capable of speech enabling multiple applications Categories and Subject Descriptors

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