Download Coarse-to-Fine Natural Language Processing by Slav Petrov (auth.) PDF

By Slav Petrov (auth.)

The impression of desktops that may comprehend typical language may be super. To boost this strength we have to be ready to immediately and successfully research quite a lot of textual content. Manually devised ideas aren't enough to supply assurance to address the advanced constitution of traditional language, necessitating platforms which can instantly research from examples. to deal with the flexibleness of traditional language, it has develop into typical perform to take advantage of statistical versions, which assign chances for instance to the several meanings of a notice or the plausibility of grammatical constructions.

This ebook develops a normal coarse-to-fine framework for studying and inference in huge statistical versions for average language processing.

Coarse-to-fine methods take advantage of a series of versions which introduce complexity progressively. on the best of the series is a trivial version within which studying and inference are either reasonable. every one next version refines the former one, until eventually a last, full-complexity version is reached. purposes of this framework to syntactic parsing, speech reputation and computer translation are provided, demonstrating the effectiveness of the procedure by way of accuracy and velocity. The ebook is meant for college kids and researchers drawn to statistical ways to usual Language Processing.

Slav’s work Coarse-to-Fine average Language Processing represents a massive strengthen within the zone of syntactic parsing, and a very good commercial for the prevalence of the machine-learning approach.

Eugene Charniak (Brown University)

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Given a (fully observed) treebank, the maximum-likelihood estimate for the probability of a rule A ! BC would simply be the ratio of the count of A to the count of the configuration A ! BC . A ! T / ŒA ! 11) with unaries estimated similarly. G/, and the expectations are taken over G’s distribution of -projected trees, P. T /jG/. Corazza and Satta (2006) do not specify how one might obtain the necessary expectations, so we give two practical methods below. G/ for any projection and PCFG G if we can calculate the expectations of the projected categories and productions according to P.

RB-U . -x FRAG-x RB-x Not NP-x DT-x NN-x this year . Fig. 1 The original parse tree (a) gets binarized (b), and then either manually annotated (c) or refined with latent variables (d) 1 Note that in parsing with the unsplit grammar, not having seen a rule doesn’t mean one gets a parse failure, but rather a possibly very weird parse (Charniak 1996). 2 Manual Grammar Refinement 11 to be captured and used to improve parse scoring. One way of capturing this kind of external context is to use parent annotation, as presented in Johnson (1998).

2 Estimating Projected Grammars Fortunately, there is a well worked-out notion of estimating a grammar from an infinite distribution over trees (Corazza and Satta 2006). G/ from the tree distribution induced by G (which can itself be estimated in any manner). The earliest work that we are aware of on estimating models from models in this way is that of Nederhof (2005), who considers the case of learning language models from other language models. Corazza and Satta (2006) extend these methods to the case of PCFGs and tree distributions.

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