31,041 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
Parsing Expression Grammars Made Practical
Parsing Expression Grammars (PEGs) define languages by specifying
recursive-descent parser that recognises them. The PEG formalism exhibits
desirable properties, such as closure under composition, built-in
disambiguation, unification of syntactic and lexical concerns, and closely
matching programmer intuition. Unfortunately, state of the art PEG parsers
struggle with left-recursive grammar rules, which are not supported by the
original definition of the formalism and can lead to infinite recursion under
naive implementations. Likewise, support for associativity and explicit
precedence is spotty. To remedy these issues, we introduce Autumn, a general
purpose PEG library that supports left-recursion, left and right associativity
and precedence rules, and does so efficiently. Furthermore, we identify infix
and postfix operators as a major source of inefficiency in left-recursive PEG
parsers and show how to tackle this problem. We also explore the extensibility
of the PEG paradigm by showing how one can easily introduce new parsing
operators and how our parser accommodates custom memoization and error handling
strategies. We compare our parser to both state of the art and battle-tested
PEG and CFG parsers, such as Rats!, Parboiled and ANTLR.Comment: "Proceedings of the International Conference on Software Language
Engineering (SLE 2015)" - 167-172 (ISBN : 978-1-4503-3686-4
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
Resolving anaphoric references on deficient syntactic descriptions
Syntactic coindexing restrictions are by now known to be of central importance to practical anaphor resolution approaches. Since, in particular due to structural ambiguity, the assumption of the availability of a unique syntactic reading proves to be unrealistic, robust anaphor resolution relies on techniques to overcome this deficiency. In this paper, two approaches are presented which generalize the verification of coindexing constraints to de cient descriptions. At first, a partly heuristic method is described, which has been implemented. Secondly, a provable complete method is specified. It provides the means to exploit the results of anaphor resolution for a further structural disambiguation. By rendering possible a parallel processing model, this method exhibits, in a general sense, a higher degree of robustness. As a practically optimal solution, a combination of the two approaches is suggested
Design and enhanced evaluation of a robust anaphor resolution algorithm
Syntactic coindexing restrictions are by now known to be of central importance to practical anaphor resolution approaches. Since, in particular due to structural ambiguity, the assumption of the availability of a unique syntactic reading proves to be unrealistic, robust anaphor resolution relies on techniques to overcome this deficiency.
This paper describes the ROSANA approach, which generalizes the verification of coindexing restrictions in order to make it applicable to the deficient syntactic descriptions that are provided by a robust state-of-the-art parser. By a formal evaluation on two corpora that differ with respect to text genre and domain, it is shown that ROSANA achieves high-quality robust coreference resolution. Moreover, by an in-depth analysis, it is proven that the robust implementation of syntactic disjoint reference is nearly optimal. The study reveals that, compared with approaches that rely on shallow preprocessing, the largely nonheuristic disjoint reference algorithmization opens up the possibility/or a slight improvement. Furthermore, it is shown that more significant gains are to be expected elsewhere, particularly from a text-genre-specific choice of preference strategies.
The performance study of the ROSANA system crucially rests on an enhanced evaluation methodology for coreference resolution systems, the development of which constitutes the second major contribution o/the paper. As a supplement to the model-theoretic scoring scheme that was developed for the Message Understanding Conference (MUC) evaluations, additional evaluation measures are defined that, on one hand, support the developer of anaphor resolution systems, and, on the other hand, shed light on application aspects of pronoun interpretation
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