Sitting at the intersection between statistics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. While Natural Language Processing increasingly relies on statistical methods, we think they have yet to use Graphical Models to their full potential. In this paper, we report on experiments in learning edit distance costs using Dynamic Bayesian Networks and present results on a pronunciation classiﬁcation task. ...
This paper presents a Bayesian decision framework that performs automatic story segmentation based on statistical modeling of one or more lexical chain features. Automatic story segmentation aims to locate the instances in time where a story ends and another begins. A lexical chain is formed by linking coherent lexical items chronologically. A story boundary is often associated with a significant number of lexical chains ending before it, starting after it, as well as a low count of chains continuing through it.
Stochastic Optimality Theory (Boersma, 1997) is a widely-used model in linguistics that did not have a theoretically sound learning method previously. In this paper, a Markov chain Monte-Carlo method is proposed for learning Stochastic OT Grammars. Following a Bayesian framework, the goal is ﬁnding the posterior distribution of the grammar given the relative frequencies of input-output pairs. The Data Augmentation algorithm allows one to simulate a joint posterior distribution by iterating two conditional sampling steps. ...
Machine transliteration is deﬁned as automatic phonetic translation of names across languages. In this paper, we propose synchronous adaptor grammar, a novel nonparametric Bayesian learning approach, for machine transliteration. This model provides a general framework without heuristic or restriction to automatically learn syllable equivalents between languages.
Many semantic parsing models use tree transformations to map between natural language and meaning representation. However, while tree transformations are central to several state-of-the-art approaches, little use has been made of the rich literature on tree automata. This paper makes the connection concrete with a tree transducer based semantic parsing model and suggests that other models can be interpreted in a similar framework, increasing the generality of their contributions.
In this paper we propose a method for the automatic decipherment of lost languages. Given a non-parallel corpus in a known related language, our model produces both alphabetic mappings and translations of words into their corresponding cognates. We employ a non-parametric Bayesian framework to simultaneously capture both low-level character mappings and highlevel morphemic correspondences.
Two models were used to apportion human cases to sources on the basis of sequence
types: the modified Hald model and the Island model (12,15). The modified Hald model
combines the prevalence of each C. jejuni sequence type among the sources with the observed
number of human isolates of that type by using a Bayesian framework (15). This model includes
source-specific and type-specific factors, and accounts for variation in the estimated prevalence.
Bayesian Estimation Theory: Basic Definitions Bayesian Estimation The Estimate–Maximise Method Cramer–Rao Bound on the Minimum Estimator Variance Design of Mixture Gaussian Models Bayesian Classification Modeling the Space of a Random Process Summary
ayesian estimation is a framework for the formulation of statistical inference problems. In the prediction or estimation of a random process from a related observation signal, the Bayesian philosophy is based on combining the evidence contained in the signal with prior knowledge of the probability distribution of the process.
This section develops an econometric framework that allows an investor to combine in-
formation in the data with prior beliefs about both pricing and skill. Nonbenchmark assets
allow us to distinguish between pricing and skill, and they supply additional information
about funds' expected returns. In addition, nonbenchmark assets help account for common
variation in funds' returns, making the investment problem feasible using a large universe
In this paper, we propose a framework for unusual event
detection. Our approach is motivated by the observation
that, while it is unrealistic to obtain a large training data
set for unusual events, it is conversely possible to do so
for usual events, allowing the creation of a well-estimated
model of usual events. In order to overcome the scarcity of
training material for unusual events, we propose the use of
Bayesian adaptation techniques , which adapt a usual
event model to produce a number of unusual event models
in an unsupervised manner.
In this work, we develop and evaluate a wide range of feature spaces for deriving Levinstyle verb classiﬁcations (Levin, 1993). We perform the classiﬁcation experiments using Bayesian Multinomial Regression (an efﬁcient log-linear modeling framework which we found to outperform SVMs for this task) with the proposed feature spaces. Our experiments suggest that subcategorization frames are not the most effective features for automatic verb classiﬁcation. A mixture of syntactic information and lexical information works best for this task. ...