1,470 research outputs found
Knowledge-based intelligent error feedback in a Spanish ICALL system
This paper describes the Spanish ICALL system ESPADA
which helps language learners to improve their syntactical
knowledge. The most important parts of ESPADA for the learner are a Demonstration Module and an Analysis Module. The Demonstration Module provides animated presentation of selected grammatical information. The Analysis Module is able to parse ill-formed sentences and to give adequate feedback on 28 different error types from different levels of language use (syntax, semantics, agreement). It contains a robust chart-based island parser which uses a combination
of mal-rules and constraint relaxation to ensure that learner input can be analysed and appropriate error feedback can be generated
A Chart-Parsing Algorithm for Efficient Semantic Analysis
In some contexts, well-formed natural language cannot be expected as input to
information or communication systems. In these contexts, the use of
grammar-independent input (sequences of uninflected semantic units like e.g.
language-independent icons) can be an answer to the users' needs. A semantic
analysis can be performed, based on lexical semantic knowledge: it is
equivalent to a dependency analysis with no syntactic or morphological clues.
However, this requires that an intelligent system should be able to interpret
this input with reasonable accuracy and in reasonable time. Here we propose a
method allowing a purely semantic-based analysis of sequences of semantic
units. It uses an algorithm inspired by the idea of ``chart parsing'' known in
Natural Language Processing, which stores intermediate parsing results in order
to bring the calculation time down. In comparison with using declarative logic
programming - where the calculation time, left to a prolog engine, is
hyperexponential -, this method brings the calculation time down to a
polynomial time, where the order depends on the valency of the predicates.Comment: 7 pages, 1 figure, LaTeX 2e using COLACL and EPSF packages.
Proceedings of the 19th International Conference on Computational Linguistics
(COLING 2002), Taipei, Republic of China (Taiwan), 24 Aug. - 1 Sept. 200
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
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
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