9,322 research outputs found
Incremental constraint-based parsing: an efficient approach for head-final languages
In this dissertation, I provide a left-to-right incremental parsing approach for Headdriven Phrase Structure Grammar (HPSG; Pollard and Sag (1987, 1994)). HPSG is a lexicalized, constraint-based theory of grammar, which has also been widely exploited in computational linguistics in recent years. Head-final languages are known to pose problems for the incrementality of head-driven parsing models, proposed for parsing with constraint-based grammar formalisms, in both psycholinguistics and computational linguistics. Therefore, here I further focus my attention on processing a head-final language, specifically Turkish, to highlight any challenges that may arise in the case of such a language. The dissertation makes two principal contributions, the first part mainly providing the theoretical treatment required for the computational approach presented in the second part. The first part of the dissertation is concerned with the analysis of certain phenomena in Turkish grammar within the frame..
Amalia -- A Unified Platform for Parsing and Generation
Contemporary linguistic theories (in particular, HPSG) are declarative in
nature: they specify constraints on permissible structures, not how such
structures are to be computed. Grammars designed under such theories are,
therefore, suitable for both parsing and generation. However, practical
implementations of such theories don't usually support bidirectional processing
of grammars. We present a grammar development system that includes a compiler
of grammars (for parsing and generation) to abstract machine instructions, and
an interpreter for the abstract machine language. The generation compiler
inverts input grammars (designed for parsing) to a form more suitable for
generation. The compiled grammars are then executed by the interpreter using
one control strategy, regardless of whether the grammar is the original or the
inverted version. We thus obtain a unified, efficient platform for developing
reversible grammars.Comment: 8 pages postscrip
Treebank-based acquisition of a Chinese lexical-functional grammar
Scaling wide-coverage, constraint-based grammars such as Lexical-Functional Grammars (LFG) (Kaplan and Bresnan, 1982; Bresnan, 2001) or Head-Driven Phrase Structure Grammars (HPSG) (Pollard and Sag, 1994) from fragments to naturally occurring unrestricted text is knowledge-intensive, time-consuming and (often prohibitively) expensive. A number of researchers have recently presented methods to automatically acquire wide-coverage, probabilistic constraint-based grammatical resources from treebanks (Cahill et al., 2002, Cahill et al., 2003; Cahill et al., 2004; Miyao et al., 2003; Miyao et al., 2004; Hockenmaier and Steedman, 2002; Hockenmaier, 2003), addressing the knowledge acquisition bottleneck in constraint-based grammar development. Research to date has concentrated on English and German. In this paper we report on an experiment to induce wide-coverage, probabilistic LFG grammatical and lexical resources for Chinese from the Penn Chinese Treebank (CTB) (Xue et al., 2002) based on an automatic f-structure annotation algorithm. Currently 96.751% of the CTB trees receive a single, covering and connected f-structure, 0.112% do not receive an f-structure due to feature clashes, while 3.137% are associated with multiple f-structure fragments. From the f-structure-annotated CTB we extract a total of 12975 lexical entries with 20 distinct subcategorisation frame types. Of these 3436 are verbal entries with a total of 11 different frame types. We extract a number of PCFG-based LFG approximations. Currently our best automatically induced grammars achieve an f-score of 81.57% against the trees in unseen articles 301-325; 86.06% f-score (all grammatical functions) and 73.98% (preds-only) against the dependencies derived from the f-structures automatically generated for the original trees in 301-325 and 82.79% (all grammatical functions) and 67.74% (preds-only) against the dependencies derived from the manually annotated gold-standard f-structures for 50 trees randomly selected from articles 301-325
Learning unification-based grammars using the Spoken English Corpus
This paper describes a grammar learning system that combines model-based and
data-driven learning within a single framework. Our results from learning
grammars using the Spoken English Corpus (SEC) suggest that combined
model-based and data-driven learning can produce a more plausible grammar than
is the case when using either learning style isolation.Comment: 10 page
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
Integrating Syntactic and Prosodic Information for the Efficient Detection of Empty Categories
We describe a number of experiments that demonstrate the usefulness of
prosodic information for a processing module which parses spoken utterances
with a feature-based grammar employing empty categories. We show that by
requiring certain prosodic properties from those positions in the input where
the presence of an empty category has to be hypothesized, a derivation can be
accomplished more efficiently. The approach has been implemented in the machine
translation project VERBMOBIL and results in a significant reduction of the
work-load for the parser.Comment: To appear in the Proceedings of Coling 1996, Copenhagen. 6 page
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