5,070 research outputs found
An Efficient Implementation of the Head-Corner Parser
This paper describes an efficient and robust implementation of a
bi-directional, head-driven parser for constraint-based grammars. This parser
is developed for the OVIS system: a Dutch spoken dialogue system in which
information about public transport can be obtained by telephone.
After a review of the motivation for head-driven parsing strategies, and
head-corner parsing in particular, a non-deterministic version of the
head-corner parser is presented. A memoization technique is applied to obtain a
fast parser. A goal-weakening technique is introduced which greatly improves
average case efficiency, both in terms of speed and space requirements.
I argue in favor of such a memoization strategy with goal-weakening in
comparison with ordinary chart-parsers because such a strategy can be applied
selectively and therefore enormously reduces the space requirements of the
parser, while no practical loss in time-efficiency is observed. On the
contrary, experiments are described in which head-corner and left-corner
parsers implemented with selective memoization and goal weakening outperform
`standard' chart parsers. The experiments include the grammar of the OVIS
system and the Alvey NL Tools grammar.
Head-corner parsing is a mix of bottom-up and top-down processing. Certain
approaches towards robust parsing require purely bottom-up processing.
Therefore, it seems that head-corner parsing is unsuitable for such robust
parsing techniques. However, it is shown how underspecification (which arises
very naturally in a logic programming environment) can be used in the
head-corner parser to allow such robust parsing techniques. A particular robust
parsing model is described which is implemented in OVIS.Comment: 31 pages, uses cl.st
An Abstract Machine for Unification Grammars
This work describes the design and implementation of an abstract machine,
Amalia, for the linguistic formalism ALE, which is based on typed feature
structures. This formalism is one of the most widely accepted in computational
linguistics and has been used for designing grammars in various linguistic
theories, most notably HPSG. Amalia is composed of data structures and a set of
instructions, augmented by a compiler from the grammatical formalism to the
abstract instructions, and a (portable) interpreter of the abstract
instructions. The effect of each instruction is defined using a low-level
language that can be executed on ordinary hardware.
The advantages of the abstract machine approach are twofold. From a
theoretical point of view, the abstract machine gives a well-defined
operational semantics to the grammatical formalism. This ensures that grammars
specified using our system are endowed with well defined meaning. It enables,
for example, to formally verify the correctness of a compiler for HPSG, given
an independent definition. From a practical point of view, Amalia is the first
system that employs a direct compilation scheme for unification grammars that
are based on typed feature structures. The use of amalia results in a much
improved performance over existing systems.
In order to test the machine on a realistic application, we have developed a
small-scale, HPSG-based grammar for a fragment of the Hebrew language, using
Amalia as the development platform. This is the first application of HPSG to a
Semitic language.Comment: Doctoral Thesis, 96 pages, many postscript figures, uses pstricks,
pst-node, psfig, fullname and a macros fil
Decompositions of Grammar Constraints
A wide range of constraints can be compactly specified using automata or
formal languages. In a sequence of recent papers, we have shown that an
effective means to reason with such specifications is to decompose them into
primitive constraints. We can then, for instance, use state of the art SAT
solvers and profit from their advanced features like fast unit propagation,
clause learning, and conflict-based search heuristics. This approach holds
promise for solving combinatorial problems in scheduling, rostering, and
configuration, as well as problems in more diverse areas like bioinformatics,
software testing and natural language processing. In addition, decomposition
may be an effective method to propagate other global constraints.Comment: Proceedings of the Twenty-Third AAAI Conference on Artificial
Intelligenc
Principles and Implementation of Deductive Parsing
We present a system for generating parsers based directly on the metaphor of
parsing as deduction. Parsing algorithms can be represented directly as
deduction systems, and a single deduction engine can interpret such deduction
systems so as to implement the corresponding parser. The method generalizes
easily to parsers for augmented phrase structure formalisms, such as
definite-clause grammars and other logic grammar formalisms, and has been used
for rapid prototyping of parsing algorithms for a variety of formalisms
including variants of tree-adjoining grammars, categorial grammars, and
lexicalized context-free grammars.Comment: 69 pages, includes full Prolog cod
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
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