User simulations are shown to be useful in spoken dialog system development. Since most current user simulations deploy probability models to mimic human user behaviors, how to set up user action probabilities in these models is a key problem to solve. One generally used approach is to estimate these probabilities from human user data. However, when building a new dialog system, usually no data or only a small amount of data is available.
This paper compares two different ways of estimating statistical language models. Many statistical NLP tagging and parsing models are estimated by maximizing the (joint) likelihood of the fully-observed training data. However, since these applications only require the conditional probability distributions, these distributions can in principle be learnt by maximizing the conditional likelihood of the training data.
In recent years there is much interest in word cooccurrence relations, such as n-grams, verbobject combinations, or cooccurrence within a limited context. This paper discusses how to estimate the probability of cooccurrences that do not occur in the training data. We present a method that makes local analogies between each specific unobserved cooccurrence and other cooccurrences that contain similar words, as determined by an appropriate word similarity metric.
Instances of a word drawn from different domains may have different sense priors (the proportions of the different senses of a word). This in turn affects the accuracy of word sense disambiguation (WSD) systems trained and applied on different domains. This paper presents a method to estimate the sense priors of words drawn from a new domain, and highlights the importance of using well calibrated probabilities when performing these estimations. By using well calibrated probabilities, we are able to estimate the sense priors effectively to achieve signiﬁcant improvements in WSD accuracy.
We present an algorithm for computing n-gram probabilities from stochastic context-free grammars, a procedure that can alleviate some of the standard problems associated with n-grams (estimation from sparse data, lack of linguistic structure, among others). The method operates via the computation of substring expectations, which in turn is accomplished by solving systems of linear equations derived from the grammar. The procedure is fully implemented and has proved viable and useful in practice. confirming its practical feasibility and utility.
We present a neural-network-based statistical parser, trained and tested on the Penn Treebank. The neural network is used to estimate the parameters of a generative model of left-corner parsing, and these parameters are used to search for the most probable parse. The parser's performance (88.8% Fmeasure) is within 1% of the best current parsers for this task, despite using a small vocabulary size (512 inputs).
The original motivation for writing this book was rather personal. The first author, in the
course of his teaching career in the Department of Pure Mathematics and Mathematical
Statistics (DPMMS), University of Cambridge, and St John’s College, Cambridge, had
many painful experiences when good (or even brilliant) students, who were interested
in the subject of mathematics and its applications and who performed well during their
first academic year, stumbled or nearly failed in the exams. This led to great frustration,
which was very hard to overcome in subsequent undergraduate years.
An important issue encountered in various branches of science is how to estimate the quantities of interest from a given ﬁnite set of uncertain (noisy) measurements. This is studied in estimation theory, which we shall discuss in this chapter. There exist many estimation techniques developed for various situations; the quantities to be estimated may be nonrandom or have some probability distributions themselves, and they may be constant or time-varying.
A very popular approach for estimating the independent component analysis (ICA) model is maximum likelihood (ML) estimation. Maximum likelihood estimation is a fundamental method of statistical estimation; a short introduction was provided in Section 4.5. One interpretation of ML estimation is that we take those parameter values as estimates that give the highest probability for the observations. In this section, we show how to apply ML estimation to ICA estimation.
Applied statistics for civil and environmental engineers has many contents: Preliminary Data Analysis, Basic Probability Concepts, Random Variables and Their Properties, Model Estimation and Testing, Methods of Regression and Multivariate Analysis, Frequency Analysis of Extreme Events, Simulation Techniques for Design, Risk and Reliability Analysis, Bayesian Decision Methods and Parameter Uncertainty.
Where the possible values could have significant impact on project’s
profitability, a decision will involve taking a risk.
In some situations, degree of risk can be objectively determined.
Estimating probability of an event usually involves subjectivity.
An effective budget should enable you to monitor progress at each phase and to identify precisely when and
why actual expenses vary from your estimate. Thus, your budget cannot be constructed on an overall project
basis; it needs to be broken down by phase.
All of the budget elements—labor, fixed expenses, and variable expenses—will vary according to the
demands of each phase. Some phases will move along relatively quickly and will require minimal team
involvement and little or no expense.
This volume pulls together and republishes, with some editing, updating, and
additions, articles written during 1978-86 for internal use within the CIA Directorate
of Intelligence. Four of the articles also appeared in the Intelligence Community
journal Studies in Intelligence during that time frame. The information is relatively
timeless and still relevant to the never-ending quest for better analysis.
The articles are based on reviewing cognitive psychology literature concerning how
people process information to make judgments on incomplete and ambiguous
Indoor air pollution poses many challenges to the health pro-
fessional. This booklet offers an overview of those challenges,
focusing on acute conditions, with patterns that point to par-
ticular agents and suggestions for appropriate remedial action.
The individual presenting with environmentally
associated symptoms is apt to have been exposed to airborne
substances originating not outdoors, but indoors. Studies from
the United States and Europe show that persons in industrial-
ized nations spend more than 90 percent of their time indoors1.
Digital signal processing is currently in a period of rapid growth caused by recent
advances inVLSI technology. This is especially true of three areas of optimum signal pro-
cessing; namely, real-time adaptive signal processing, eigenvector methods of spectrum
estimation, and parallel processor implementations of optimum filtering and prediction
In this edition the book has been brought up to date by increasing the emphasis
on the above areas and including several new developments.
It is difﬁ cult to estimate the number of adult ESOL students in the
United States because many are highly mobile and some are undocu-
mented. According to the National Center for ESL Literacy Education,
“The most recent statistics from the U.S. Department of Education, Ofﬁ ce
of Vocational and Adult Education, show that 1,119,589 learners were
enrolled in federally funded, state-administered adult ESL classes. This
represents 42% of the enrollment in federally funded, state-administered
adult education classes” (Florez, personal communication, 2001).
Applied statistics and probabilty for engineers_This is an introductory textbook for a first course in applied statistics and probability for undergraduate students in engineering and the physical or chemical sciences. These individuals play a significant role in designing and developing new products and manufacturing systems and processes, and they also improve existing systems. Statistical methods are an important tool in these activities because they provide the engineer with both descriptive and analytical methods for dealing with the variability in observed data.
This paper describes an extension to the hidden Markov model for part-of-speech tagging using second-order approximations for both contextual and lexical probabilities. This model increases the accuracy of the tagger to state of the art levels. These approximations make use of more contextual information than standard statistical systems. New methods of smoothing the estimated probabilities are also introduced to address the sparse data problem.