1,349 research outputs found
Edge-Based Best-First Chart Parsing
Best-first probabilistic chart parsing attempts to parse efficiently by working on edges that are judged 'best' by some probabilistic figure of merit (FOM). Recent work has used proba- bilistic context-free grammars (PCFGs) to sign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to us- ing a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much finergrained control over parsing effort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results -- that is, our parser achieves equivalent results using one twentieth the number of edges. Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing
Measuring efficiency in high-accuracy, broad-coverage statistical parsing
Very little attention has been paid to the comparison of efficiency between
high accuracy statistical parsers. This paper proposes one machine-independent
metric that is general enough to allow comparisons across very different
parsing architectures. This metric, which we call ``events considered'',
measures the number of ``events'', however they are defined for a particular
parser, for which a probability must be calculated, in order to find the parse.
It is applicable to single-pass or multi-stage parsers. We discuss the
advantages of the metric, and demonstrate its usefulness by using it to compare
two parsers which differ in several fundamental ways.Comment: 8 pages, 4 figures, 2 table
Global Thresholding and Multiple Pass Parsing
We present a variation on classic beam thresholding techniques that is up to
an order of magnitude faster than the traditional method, at the same
performance level. We also present a new thresholding technique, global
thresholding, which, combined with the new beam thresholding, gives an
additional factor of two improvement, and a novel technique, multiple pass
parsing, that can be combined with the others to yield yet another 50%
improvement. We use a new search algorithm to simultaneously optimize the
thresholding parameters of the various algorithms.Comment: Fixed latex errors; fixed minor errors in published versio
An Application of Probabilistic Grammars to Efficient Machne Translation
In this paper we present one of the algorithms used to parse probabilistic context-free grammars: the A* parsing algorithm, which is based on the A* graph search method. We show an example of application of the algorithm in an existing machine translation system. The existing CYK-based parser used in the Translatica system was modified by applying the A* parsing algorithm in order to examine the possibilities of improving its performance. This paper presents the results of applying the A* algorithm with different heuristic functions and their impact on the performance of the parser
A New Statistical Parser Based on Bigram Lexical Dependencies
This paper describes a new statistical parser which is based on probabilities
of dependencies between head-words in the parse tree. Standard bigram
probability estimation techniques are extended to calculate probabilities of
dependencies between pairs of words. Tests using Wall Street Journal data show
that the method performs at least as well as SPATTER (Magerman 95, Jelinek et
al 94), which has the best published results for a statistical parser on this
task. The simplicity of the approach means the model trains on 40,000 sentences
in under 15 minutes. With a beam search strategy parsing speed can be improved
to over 200 sentences a minute with negligible loss in accuracy.Comment: 8 pages, to appear in Proceedings of ACL 96. Uuencoded gz-compressed
postscript file created by csh script uufile
Discontinuous Data-Oriented Parsing: A mildly context-sensitive all-fragments grammar
Recent advances in parsing technology have made treebank parsing with discontinuous constituents possible, with parser output of competitive quality (Kallmeyer and Maier, 2010). We apply Data-Oriented Parsing (DOP) to a grammar formalism that allows for discontinuous trees (LCFRS). Decisions during parsing are conditioned on all possible fragments, resulting in improved performance. Despite the fact that both DOP and discontinuity present formidable challenges in terms of computational complexity, the model is reasonably efficient, and surpasses the state of the art in discontinuous parsing.
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