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parsing with input-driven selection.
slava bloom, hal daum iii, and jason eisner (2012).
in the 17th aaai conference on artificial intelligence,

in one-pass parsing the parser keeps track of its grammatical success and updates the parser’s internal representation in order to make it more likely to succeed in a subsequent pass. we propose a model of parsing in which grammatical success is maximized through heuristic decision making. this decision making is guided by a process that samples from a dirichlet prior distribution over parser output representations. the posterior distribution for a representation is deterministically used to determine which actions to take. we formalize the structure of the posterior, which assumes a multinomial distribution over representations, and we derive the algorithm for sampling the posterior distribution. we describe it as a form of supervised stochastic context-free parsing. we identify major sources of error in such a parser, including memory errors (aliasing) in that sampling error is encoded in a parser’s internal representation, and incorrect readings due to lack of coverage (decomposition) in that the parser cannot perform any constituent selection in the posterior. we demonstrate the technique on the penn tree bank and show that the technique has significant error-preventing power. we also demonstrate it on a new benchmark, a russian corpus of about 50,000 words of surface syntax and about 250,000 sentences of full syntax.

note: the author(s) did not report any preregistration information, but has apparently submitted an extended version to tptp.

keywords: ccg parsing, trn+dyn, s&r+cp+dyn, mug+wish, selected papers

parsing with input-driven selection. slava bloom, hal daum iii, and jason eisner (2012). in the 17th aaai conference on artificial intelligence, in one-pass parsing the parser keeps track of its grammatical success and updates the parser’s internal representation in order to make it more likely to succeed in a subsequent pass. we propose a model of parsing in which grammatical success is maximized through heuristic decision making. this decision making is guided by a process that samples from a dirichlet prior distribution over parser output representations. the posterior distribution for a representation is deterministically used to determine which actions to take. we formalize the structure of the posterior, which assumes a multinomial distribution over representations, and we derive the algorithm for sampling the posterior distribution. we describe it as a form of supervised stochastic context-free parsing. we identify major sources of error in such a parser, including memory errors (aliasing) in that sampling error is encoded in a parser’s internal representation, and incorrect readings due to lack of coverage (decomposition) in that the parser cannot perform any constituent selection in the posterior. we demonstrate the technique on the penn tree bank and show that the technique has significant error-preventing power. we also demonstrate it on a new benchmark, a russian corpus of about 50,000 words of surface syntax and about 250,000 sentences of full syntax. note: the author(s) did not report any preregistration information, but has apparently submitted an extended version to tptp. keywords: ccg parsing, trn+dyn, s&r+cp+dyn, mug+wish, selected papers
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