Download Grammars for Language and Genes: Theoretical and Empirical by David Chiang PDF

By David Chiang

Grammars are gaining significance in normal language processing and computational biology as a method of encoding theories and structuring algorithms. yet one severe challenge to functions of grammars is that formal language concept normally classifies grammars based on their susceptible generative potential (what units of strings they generate) and has a tendency to disregard powerful generative potential (what units of structural descriptions they generate) even if the latter is extra appropriate to purposes.

This publication develops and demonstrates a framework for conducting rigorous comparisons of grammar formalisms when it comes to their usefulness for purposes, targeting 3 parts of program: statistical parsing, normal language translation, and organic series research. those effects may still pave the way in which for theoretical study to pursue effects which are extra directed in the direction of functions, and for functional study to discover using complicated grammar formalisms extra easily.

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Extra resources for Grammars for Language and Genes: Theoretical and Empirical Investigations

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The standard policy is to treat the sentence as if it were not in either the gold standard file or the test file, but the more rigorous policy used here is to keep the sentence but treat the test file as if it had not guessed any brackets. For the WSJ corpus, the scores are not affected, but in this case, they are affected slightly. 2% for sentences of length ≤ 40. 2 Training by Expectation-Maximization In the type of approach we have been discussing so far, we use hand-written rules to reconstruct TIG-SA structural descriptions from the partial structural descriptions in training data, and then train the TIG-SA model by maximizing the likelihood of the reconstructed training data according to the model.

3 A Probabilistic TIG Model 35 the prepositional object, in the hope that the latter will help make PP attachment decisions [46]. 3 for a TIG derivation of the latter example). Third, by decoupling the reconstruction heuristics from the training process proper, this new view suggests alternative training methods. Below we describe experiments using Expectation-Maximization to train directly on the observed, not the reconstructed data (a method explored previously by Hwa [63] for TIG). Basic definition The parameters of a probabilistic TAG model [105, 112] are: ∑ Pi (α) = 1 α ∑ Ps (α | η) = 1 α ∑ Pa (β | η) + Pa (NONE | η) = 1 β where α ranges over initial trees, β over auxiliary trees, and η over nodes.

Xn with decoration Δ1 , . . , Δn , construct the CFG rules X(α, η) → X1 (α1 , η1 ), . . 2 Lexicalized Probabilistic CFG 33 If we further assume that each elementary tree α has a single lexical anchor wα , then observe that (α, η) subsumes (wα ), so that the CFG with rules X(wα ) → X1 (wα1 ), . . , Xn (wαn ) for all αi , ηi ∈ Δi generates an approximate superset of the original TSG. But this grammar is none other than a lexicalized PCFG. We may therefore think of a lexicalized PCFG as an approximate cover of a TSG.

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