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- W39596511 abstract "The maximum-entropy framework provides great flexibility in specifying what features a model may take into account, making it effective for a wide range of natural language processing tasks. But because parameter estimation in this framework involves computations over the whole space of possible labelings, it is unwieldy for the parsing problem, where this space is very large. Researchers have tried several strategies for efficiently training parsing models in the maximum-entropy framework. Ratnaparkhi’s parser (1997) models the probabilities of actions of a pushdown automaton instead of the probabilities of entire parses, but for this reason is susceptible to the label-bias problem (Lafferty et al. 2001). Abney (1997) proposes random sampling of the parse space. Johnson et al. (1999) propose using conditional estimation instead of joint estimation. This reduces the space to the possible parses of a single sentence, which is much smaller but can still be unmanageably large for many grammars. Collins’ discriminative models (2000) are cast as parse-reranking models instead of parsing models, and are therefore dependent on his earlier generative model (Collins 1999). Geman and Johnson (2002) and Miyao and Tsujii (2002) propose compact representations of the parse space that make processing" @default.
- W39596511 created "2016-06-24" @default.
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- W39596511 date "2008-01-01" @default.
- W39596511 modified "2023-09-27" @default.
- W39596511 title "Mildly Context-Sensitive Grammars for Estimating Maximum Entropy Parsing Models" @default.
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