Buo, F. D. (1996). Feaspar - a feature structure parser learning to parse spoken language. In Proceedings of COLING-96. Home/Search Document Details and Download
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This paper is cited in the following contexts:
Literature Survey - Kool (1999) (1 citation) (Correct)
....the ability to look for an unknown word and impose syntactic roles from the category of the unknown word. Other publications by the same author(s) on connectionist parsing are (Johnson, Kwasny, and Kalman, 1993) and (Johnson, Kwasny, and Kalman, Waibel et al. (Buo, Polzin, and Waibel, 1994; Buo and Waibel, 1996) describe a system (FeasPar Feature structure Parser) that learns to parse spontaneous speech, which consists of combined neural networks. The networks split the incoming sentence into chunks, which are labeled with feature values and chunck relations. Then the search nds the most probable and ....
Buo, F.D. and A. Waibel. 1996. Feaspar - a feature structure parser learning to parse spoken language. In Proceedings of the COLING.
Experiments with Learning Parsing Heuristics - Delisle, LETOURNEAU, Matwin (1998) (Correct)
....learning systems. A distinctive feature of our work is the fact that we used machine learning techniques to improve an existing rule based natural language processor from the inside. This contrasts with approaches where there are essentially no explicit rules, such as neural networks (e.g. [Buo 1996]) or approaches where the machine learning algorithms attempt to infer via deduction (e.g. Samuelsson 1994] induction (e.g. Theeramunkong et al. 1997] Zelle Mooney 1994] under user cooperation (e.g. Simmons Yu 1992] Hermjakob Mooney 1997] transformation based error driven ....
Buo F.D. (1996) "FeasPar---A Feature Structure Parser Learning to Parse Spontaneous Speech", Ph.D. Thesis, Fakultt fr Informatik, Univ. Karlsruhe, Germany.
Balancing Robustness and Efficiency in Unification-augmented.. - Rosé, Lavie (4 citations) (Correct)
....disfluencies and can extract the most complete interpretation possible from a given input. Researchers have approached the robust parsing problem from a variety of different directions, including symbolic [28, 17, 3, 25, 23, 20, 18, 12] statistical [5, 24, 26, 30, 10, 19, 31] and connec1 tionist [11, 6, 14, 15]. While statistical and connectionist approaches are inherently robust, and can often be trained automatically from labeled corpora, symbolic parsers are most capable of performing deep and detailed analysis based on linguistic principles. Thus, symbolic parsers are still highly attractive for ....
F. D. Buo. Feaspar - a feature structure parser learning to parse spoken language. In Proceedings of COLING-96, 1996.
Balancing Robustness and Efficiency in Unification-Augmented.. - Rose, Lavie (Correct)
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Buo, F. D. (1996). Feaspar - a feature structure parser learning to parse spoken language. In Proceedings of COLING-96.
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