531,584 research outputs found
Constraint Logic Programming for Natural Language Processing
This paper proposes an evaluation of the adequacy of the constraint logic
programming paradigm for natural language processing. Theoretical aspects of
this question have been discussed in several works. We adopt here a pragmatic
point of view and our argumentation relies on concrete solutions. Using actual
contraints (in the CLP sense) is neither easy nor direct. However, CLP can
improve parsing techniques in several aspects such as concision, control,
efficiency or direct representation of linguistic formalism. This discussion is
illustrated by several examples and the presentation of an HPSG parser.Comment: 15 pages, uuencoded and compressed postscript to appear in
Proceedings of the 5th Int. Workshop on Natural Language Understanding and
Logic Programming. Lisbon, Portugal. 199
Memoization in Constraint Logic Programming
This paper shows how to apply memoization (caching of subgoals and associated
answer substitutions) in a constraint logic programming setting. The research
is is motivated by the desire to apply constraint logic programming (CLP) to
problems in natural language processing that involve (constraint) interleaving
or coroutining, such as GB and HPSG parsing.Comment: 11 page
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
A commentary on "The now-or-never bottleneck: a fundamental constraint on language", by Christiansen and Chater (2016)
In a recent article, Christiansen and Chater (2016) present a fundamental
constraint on language, i.e. a now-or-never bottleneck that arises from our
fleeting memory, and explore its implications, e.g., chunk-and-pass processing,
outlining a framework that promises to unify different areas of research. Here
we explore additional support for this constraint and suggest further
connections from quantitative linguistics and information theory
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