8 research outputs found
Efficient Algorithms for Parsing the DOP Model
Excellent results have been reported for Data-Oriented Parsing (DOP) of
natural language texts (Bod, 1993). Unfortunately, existing algorithms are both
computationally intensive and difficult to implement. Previous algorithms are
expensive due to two factors: the exponential number of rules that must be
generated and the use of a Monte Carlo parsing algorithm. In this paper we
solve the first problem by a novel reduction of the DOP model to a small,
equivalent probabilistic context-free grammar. We solve the second problem by a
novel deterministic parsing strategy that maximizes the expected number of
correct constituents, rather than the probability of a correct parse tree.
Using the optimizations, experiments yield a 97% crossing brackets rate and 88%
zero crossing brackets rate. This differs significantly from the results
reported by Bod, and is comparable to results from a duplication of Pereira and
Schabes's (1992) experiment on the same data. We show that Bod's results are at
least partially due to an extremely fortuitous choice of test data, and
partially due to using cleaner data than other researchers.Comment: 10 page
Parsing Inside-Out
The inside-outside probabilities are typically used for reestimating
Probabilistic Context Free Grammars (PCFGs), just as the forward-backward
probabilities are typically used for reestimating HMMs. I show several novel
uses, including improving parser accuracy by matching parsing algorithms to
evaluation criteria; speeding up DOP parsing by 500 times; and 30 times faster
PCFG thresholding at a given accuracy level. I also give an elegant,
state-of-the-art grammar formalism, which can be used to compute inside-outside
probabilities; and a parser description formalism, which makes it easy to
derive inside-outside formulas and many others.Comment: Ph.D. Thesis, 257 pages, 40 postscript figure
Learning Efficient Disambiguation
This dissertation analyses the computational properties of current
performance-models of natural language parsing, in particular Data Oriented
Parsing (DOP), points out some of their major shortcomings and suggests
suitable solutions. It provides proofs that various problems of probabilistic
disambiguation are NP-Complete under instances of these performance-models, and
it argues that none of these models accounts for attractive efficiency
properties of human language processing in limited domains, e.g. that frequent
inputs are usually processed faster than infrequent ones. The central
hypothesis of this dissertation is that these shortcomings can be eliminated by
specializing the performance-models to the limited domains. The dissertation
addresses "grammar and model specialization" and presents a new framework, the
Ambiguity-Reduction Specialization (ARS) framework, that formulates the
necessary and sufficient conditions for successful specialization. The
framework is instantiated into specialization algorithms and applied to
specializing DOP. Novelties of these learning algorithms are 1) they limit the
hypotheses-space to include only "safe" models, 2) are expressed as constrained
optimization formulae that minimize the entropy of the training tree-bank given
the specialized grammar, under the constraint that the size of the specialized
model does not exceed a predefined maximum, and 3) they enable integrating the
specialized model with the original one in a complementary manner. The
dissertation provides experiments with initial implementations and compares the
resulting Specialized DOP (SDOP) models to the original DOP models with
encouraging results.Comment: 222 page
Data-oriented models of parsing and translation
The merits of combining the positive elements of the rule-based and data-driven approaches to MT are clear: a combined model has the potential to be highly accurate, robust, cost-effective to build and adaptable. While the merits are clear, however, how best to combine these techniques into a model which retains the positive characteristics of each approach, while inheriting as few of the disadvantages as possible, remains an unsolved problem. One possible solution to this challenge is the Data-Oriented Translation (DOT) model originally proposed by Poutsma (1998, 2000, 2003), which is based on Data-Oriented Parsing (DOP) (e.g. (Bod, 1992; Bod et al., 2003)) and combines examples, linguistic information and a statistical translation model.
In this thesis, we seek to establish how the DOT model of translation relates to the other main MT methodologies currently in use. We find that this model differs from other hybrid models of MT in that it inextricably interweaves the philosophies of the rule-based, example-based and statistical approaches in an integrated framework.
Although DOT embodies many positive characteristics on a theoretical level, it also inherits the computational complexity associated with DOP. Previous experiments assessing the performance of the DOT model of translation were small in scale and the training data used was not ideally suited to the task (Poutsma, 2000, 2003). However, the algorithmic limitations of the DOT implementation used to perform these experiments prevented a more informative assessment from being carried out. In this thesis, we look to the innovative solutions developed to meet the challenges of implementing the DOP model, and investigate their application to DOT. This investigation culminates in the development of a DOT system; this system allows us to perform translation experiments which are on a larger scale and incorporate greater translational complexity than heretofore. Our evaluation indicates that the positive characteristics of the model identified on a theoretical level are also in evidence when it is subjected to empirical assessment. For example, in terms of exact match accuracy, the DOT model outperforms an SMT model trained and tested on the same data by up to 89.73%.
The DOP and DOT models for which we provide empirical evaluations assume contextfree phrase-structure tree representations. However, such models can also be developed for more sophisticated linguistic formalisms. In this thesis, we also focus on the efforts which have been made to integrate the representations of Lexical-Functional Grammar (LFG) with DOP and DOT. We investigate the usefulness of the algorithms developed for DOP (and adapted here to Tree-DOT) when implementing the (more complex) LFG-DOP and LFG-DOT models. We examine how constraints are employed in these models for more accurate disambiguation and seek an alternative methodology for improved constraint specification. We also hypothesise as to how the constraints used to predict both good parses and good translations might be pruned in a motivated fashion. Finally, we explore the relationship between translational equivalence and limited generalisation reusability for both the tree-based and LFG-based DOT models, focussing on how this relationship differs depending on which formalism is assumed