Dialogue act classification is a central challenge for dialogue systems. Although the importance of emotion in human dialogue is widely recognized, most dialogue act classification models make limited or no use of affective channels in dialogue act classification. This paper presents a novel affect-enriched dialogue act classifier for task-oriented dialogue that models facial expressions of users, in particular, facial expressions related to confusion.
We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a second “reranking” model is then used to choose an utterance from these 1000-best lists. The reranking model makes use of syntactic features together with a parameter estimation method that is based on the perceptron algorithm. We describe experiments on the Switchboard speech recognition task. ...
We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which produce power-law distributions more closely resembling those in natural languages. We show that an approximation to the hierarchical Pitman-Yor language model recovers the exact formulation of interpolated Kneser-Ney, one of the best smoothing methods for n-gram language models.
When Flash Player 9 released in June 2006, it introduced the new scripting language, ActionScript 3, which has already taken hold in the Adobe Flex application development community. ActionScript 3 provides not only a significant enhancement in performance, but also a more robust programming model that lends itself to complex Rich Internet Application development. For web designers and developers who need to make the move to ActionScript 3 from the previous version, ActionScript 2, the learning curve has proven to be significant.
Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams.
This book reports initial efforts in providing some useful extensions in financial
modeling; further work is necessary to complete the research agenda.
The demonstrated extensions in this book in the computation and modeling
of optimal control in finance have shown the need and potential for further
areas of study in financial modeling. Potentials are in both the mathematical
structure and computational aspects of dynamic optimization. There are needs
for more organized and coordinated computational approaches.
There is a bustling atmosphere in the headquarters of the globally active Confusio
Corporation. Everything seems to be just fine. Yet, there is a bad atmosphere in
the precious wood-paneled conference room of the managing director Paul Peppy.
Peppy has drummed together his top managers from all important branch offices; a
hard and uncompromising crackdown is urgently required! Concerning the topic of
the crisis summit, he has intentionally left the participants in the dark.
Information about climate1 is used to make decisions every day. From farmers deciding
which crops to plant next season to mayors in large cities deciding how to prepare for future heat
waves, and from an insurance company assessing future flood risks to a national security planner
assessing future conflict risks from the impacts of drought, users of climate information span a
vast array of sectors in both the public and private spheres. Each of these communities has
different needs for climate data, with different time horizons (see Box 1) and different tolerances
“ A Developer’s Guide to Data Modeling for SQL Server explains the concepts and practice of data modeling with a clarity that makes the technology accessible to anyone building databases and data-driven applications.
“Eric Johnson and Joshua Jones combine a deep understanding of the science of data modeling with the art that comes with years of experience. If you’re new to data modeling, or find the need to brush up on its concepts, this book is for you.”
—Peter Varhol, Executive Editor, Redmond Magazine ...
This book demonstrates applications and case studies performed by experts for professionals and students in the field of technology, engineering, materials, decision making management and other industries in which mathematical modelling plays a role. Each chapter discusses an example and these are ranging from well-known standards to novelty applications. Models are developed and analysed in details, authors carefully consider the procedure for constructing a mathematical replacement of phenomenon under consideration. ...
This book has been made possible by a sea of efforts. Collating this book was a labour of love. I share the
topic of Decision-making support systems with the reader with a sense of zeal and oceans of enthusiasm.
I think that these attributes are reflected in this book and perhaps make it better.
I wish to thank Sophie Tergeist from Bookboon Ltd for her guidance and Shafaqat Hussain for designing
the cover of this book.
Each chapter in this book was subject to a previous peer-review process.
[ Team LiB ] 7.4 Conditional Statements Conditional statements are used for making decisions based upon certain conditions. These conditions are used to decide whether or not a statement should be executed. Keywords if and else are used for conditional statements.
Consumer decision-making is defined as the behaviour patterns of consumers that precede, determine and follow the decision making process for the acquisition of need satisfying products, ideas or services (Du Plessis & Rousseau, 1999). During the consumer decision-making process, not only do consumers make decisions regarding which brand options to choose but they also decide what quantity of the good to purchase.
Statistical Prediction Models
Bayes' theorem, as presented above, deals with a clinical prediction problem that is unrealistically simple relative to most problems a clinician faces. Prediction models, based on multivariable statistical models, can handle much more complex problems and substantially enhance predictive accuracy for specific situations. Their particular advantage is the ability to take into account many overlapping pieces of information and assign a relative weight to each based on its unique contribution to the prediction in question.
Most statistical machine translation systems rely on composed rules (rules that can be formed out of smaller rules in the grammar). Though this practice improves translation by weakening independence assumptions in the translation model, it nevertheless results in huge, redundant grammars, making both training and decoding ineﬃcient. Here, we take the opposite approach, where we only use minimal rules (those that cannot be formed out of other rules), and instead rely on a rule Markov model of the derivation history to capture dependencies between minimal rules. ...
We consider the problem of correcting errors made by English as a Second Language (ESL) writers and address two issues that are essential to making progress in ESL error correction - algorithm selection and model adaptation to the ﬁrst language of the ESL learner. A variety of learning algorithms have been applied to correct ESL mistakes, but often comparisons were made between incomparable data sets. We conduct an extensive, fair comparison of four popular learning methods for the task, reversing conclusions from earlier evaluations. ...