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Abstract
This paper presents a method for speeding up a deep parser through backbone extraction and pruning based on CFG ambiguity packing. The TRIPS grammar is a wide-coverage grammar for deep natural language understanding in dialogue, utilized in 6 different application domains, and with high coverage and sentence-level accuracy on human-human task-oriented dialogue corpora (Dzikovska, 2004). The TRIPS parser uses a best-first beam search algorithm and a chart size limit, both of which are a form of pruning focused on finding an n-best list of interpretations. However, for longer sentences limiting the chart size results in failed parses, while increasing the chart size limits significantly impacts the parsing speed.
Dialogue systems for health communication hold out the promise of providing intelligent assistance to patients through natural interfaces that require no training to use. But in order to make the development of such systems cost effective, we must be able to use generic techniques and components which are then specialized as needed to the specific health problem and patient population. In this paper, we describe Chester, a prototype intelligent assistant that interacts with its user via conversational natural spoken language to provide them with information and advice regarding their prescribed medications. Chester builds on our prior experience constructing conversational assistants in other domains. The emphasis of this paper is on the portability of our generic spoken dialogue technology, and presents a case study of the application of these techniques to the development of a dialogue system for health communication.
We describe a method for annotating spoken dialog corpora using both automatic and manual annotation. Our semi-automated method for corpus development results in a corpus combining rich semantics, discourse information and reference annotation, and allows us to explore issues relating these. 1
We describe a simple approach for integrating shallow and deep parsing. We use phrase structure bracketing obtained from the Collins parser as filters to guide deep parsing. Our experiments demonstrate that our technique yields substantial gains in speed along with modest improvements in accuracy.
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 demonstra...
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 func-tion 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 flex-ibility 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 combination of a beam search together with am-biguity packing and statistical disambiguation provide a flexible frame-work for achieving a good balance between robustness and efficiency in such parsers. Our investigations are based on experimental results and comparative performance evaluations of both parsers using a grammar for the spoken language ESST (English Spontaneous Scheduling Task) domain.
Skeletons in the parser: Using a shallow parser to improve deep parsing
- M Swift
- J Allen
- D Gildea
M. Swift, J. Allen, and D. Gildea. 2004. Skeletons in
the parser: Using a shallow parser to improve deep
parsing. In Proceedings of COLING-04.
Discourse annotation in the monroe corpus
- J Tetreault
- M Swift
- P Prithviraj
- M Dzikovska
- J Allen
J. Tetreault, M. Swift, P. Prithviraj, M. Dzikovska, and J.
Allen. 2004. Discourse annotation in the monroe corpus. In ACL-04 workshop on Discourse Annotation.