A Variant of Earley Parsing

The Earley algorithm is a widely used parsing method in natural language processing applications. We introduce a variant of Earley parsing that is based on a ``delayed'' recognition of constituents. This allows us to start the recognition of a constituent only in cases in which all of its subconstituents have been found within the input string. This is particularly advantageous in several cases in which partial analysis of a constituent cannot be completed and in general in all cases of productions sharing some suffix of their right-hand sides (even for different left-hand side nonterminals). Although the two algorithms result in the same asymptotic time and space complexity, from a practical perspective our algorithm improves the time and space requirements of the original method, as shown by reported experimental results.Comment: 12 pages, 1 Postscript figure, uses psfig.tex and llncs.st

LATE Ain'T Earley: A Faster Parallel Earley Parser

We present the LATE algorithm, an asynchronous variant of the Earley algorithm for parsing context-free grammars. The Earley algorithm is naturally task-based, but is difficult to parallelize because of dependencies between the tasks. We present the LATE algorithm, which uses additional data structures to maintain information about the state of the parse so that work items may be processed in any order. This property allows the LATE algorithm to be sped up using task parallelism. We show that the LATE algorithm can achieve a 120x speedup over the Earley algorithm on a natural language task

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

Probabilistic Constraint Logic Programming

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-linear probability model for probabilistic constraint logic programming. On top of this model we define an algorithm to estimate the parameters and to select the properties of log-linear models from incomplete data. This algorithm is an extension of the improved iterative scaling algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm applies to log-linear models in general and is accompanied with suitable approximation methods when applied to large data spaces. Furthermore, we present an approach for searching for most probable analyses of the probabilistic constraint logic programming model. This method can be applied to the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl