Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Linguistic knowledge acquisition from parsing failures
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Automatic acquisition of subcategorization frames from untagged text
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Hypothesis selection in grammar acquisition
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
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The framework we adopted for customizing linguistic knowledge to individual application domains is an integration of symbolic and statistical approaches. In order to acquire domain specific knowledge, we have previously proposed a rule-based mechanism to hypothesize missing knowledge from partial parsing results of unsuccessfully parsed sentences. In this paper, we focus on the statistical process which selects plausible knowledge from a set of hypotheses generated from the whole corpus. In particular, we introduce two statistical measures of hypotheses, Local Plausibility, and Global Plausibility, and describe how these measures are determined iteratively. The proposed method will be incorporated into the tool kit for linguistic knowledge acquisition which we are now developing.