By Slav Petrov (auth.)
The influence of desktops that may comprehend normal language can be great. To increase this power we have to be capable of immediately and successfully study quite a lot of textual content. Manually devised ideas aren't adequate to supply assurance to deal with the advanced constitution of average language, necessitating structures that may instantly examine from examples. to deal with the pliability of ordinary language, it has turn into regular perform to exploit statistical types, which assign possibilities for instance to the various meanings of a observe or the plausibility of grammatical constructions.
This booklet develops a basic coarse-to-fine framework for studying and inference in huge statistical versions for ordinary language processing.
Coarse-to-fine methods make the most a series of versions which introduce complexity progressively. on the most sensible of the series is a trivial version within which studying and inference are either reasonable. every one next version refines the former one, till a last, full-complexity version is reached. purposes of this framework to syntactic parsing, speech reputation and computing device translation are provided, demonstrating the effectiveness of the method by way of accuracy and velocity. The publication is meant for college students and researchers drawn to statistical techniques to typical Language Processing.
Slav’s work Coarse-to-Fine usual Language Processing represents a massive increase within the quarter of syntactic parsing, and a good commercial for the prevalence of the machine-learning approach.
Eugene Charniak (Brown University)
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Extra info for Coarse-to-Fine Natural Language Processing
G/ to the distribution G induces over -projected trees: P. T /jG/. Since the math is worked out in detail in Corazza and Satta (2006), including questions of when the resulting estimates are proper, we refer the reader to their excellent presentation for more details. The proofs of the general case are given in Corazza and Satta (2006), but the resulting procedure is quite intuitive. 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 !
For instance, Matsuzaki et al. 03) Fig. 2 Evolution of the DT tag during hierarchical splitting and merging. 3 If these manual refinements are good, they reduce the search space for EM by constraining it to a smaller region. On the other hand, this pre-splitting defeats some of the purpose of automatically learning latent subcategories, leaving to the user the task of guessing what a good starting grammar might be, and potentially introducing overly fragmented subcategories. Instead, we take a fully automated, hierarchical approach where we repeatedly split and re-train the grammar.
The contributions of our method are that we derive sequences of refinements in a new way (Sect. 1), we consider refinements which are themselves complex, and, because our full grammar is not impossible to parse with, we automatically tune the pruning thresholds on held-out data. It should be noted that other techniques for improving inference could also be applied here. In particular, A* parsing techniques (Klein and Manning 2003b; Haghighi et al. 2007) appear very appealing because of their guaranteed optimality.
Coarse-to-Fine Natural Language Processing by Slav Petrov (auth.)