4,252 research outputs found
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
Evaluation of Computational Grammar Formalisms for Indian Languages
Natural Language Parsing has been the most prominent research area since the genesis of Natural Language Processing. Probabilistic Parsers are being developed to make the process of parser development much easier, accurate and fast. In Indian context, identification of which Computational Grammar Formalism is to be used is still a question which needs to be answered. In this paper we focus on this problem and try to analyze different formalisms for Indian languages
Bayesian Grammar Induction for Language Modeling
We describe a corpus-based induction algorithm for probabilistic context-free
grammars. The algorithm employs a greedy heuristic search within a Bayesian
framework, and a post-pass using the Inside-Outside algorithm. We compare the
performance of our algorithm to n-gram models and the Inside-Outside algorithm
in three language modeling tasks. In two of the tasks, the training data is
generated by a probabilistic context-free grammar and in both tasks our
algorithm outperforms the other techniques. The third task involves
naturally-occurring data, and in this task our algorithm does not perform as
well as n-gram models but vastly outperforms the Inside-Outside algorithm.Comment: 8 pages, LaTeX, uses aclap.st
- …