An exploration into the client at the heart of therapy : a qualitative perspective

Over 50 years ago Eysenck challenged the existing base of research into psychotherapy. Since that time, a large number of investigations have been conducted to verify the efficacy of therapy. Recently however, an increasing number of studies have cast new doubts on this research base. Instead of therapy being a function of the therapist, it is now becoming ever more apparent that the client plays a prime role in the therapeutic process. The qualitative studies presented in this paper provide some examples of research that demonstrates that clients are actively involved in their therapy, even making counselling work despite their counsellor. These studies suggest that clients may not experience therapy as beneficially as traditional outcome studies indicate. This raises a new challenge to researchers to more fully explore the client's experience of therapy, a challenge to which qualitative methods of inquiry would appear well suited

Improving Multiclass Text Classification with the Support Vector Machine

We compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM's lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties

Extracting information from informal communication

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (leaves 89-93).This thesis focuses on the problem of extracting information from informal communication. Textual informal communication, such as e-mail, bulletin boards and blogs, has become a vast information resource. However, such information is poorly organized and difficult for a computer to understand due to lack of editing and structure. Thus, techniques which work well for formal text, such as newspaper articles, may be considered insufficient on informal text. One focus of ours is to attempt to advance the state-of-the-art for sub-problems of the information extraction task. We make contributions to the problems of named entity extraction, co-reference resolution and context tracking. We channel our efforts toward methods which are particularly applicable to informal communication. We also consider a type of information which is somewhat unique to informal communication: preferences and opinions. Individuals often expression their opinions on products and services in such communication. Others' may read these "reviews" to try to predict their own experiences. However, humans do a poor job of aggregating and generalizing large sets of data. We develop techniques that can perform the job of predicting unobserved opinions.(cont.) We address both the single-user case where information about the items is known, and the multi-user case where we can generalize opinions without external information. Experiments on large-scale rating data sets validate our approach.by Jason D.M. Rennie.Ph.D

Injection mould tool manufacture in less than five days

Using novel rapid prototyping (RP) technology combined with established electroforming tehniques and electro-discharge machining (EDM), injection mould tools have been produced in days rather than weeks. These moulds are manufactured in new silicon-aluminium alloys developed by Osprey Metals, containing 50% or more silicon. The synthesis of these processes shows great potential for use in the rapid tooling sector

Bayesian performance comparison of text classifiers

How can we know whether one classifier is really better than the other? In the area of text classification, since the publication of Yang and Liu's seminal SIGIR-1999 paper, it has become a standard practice for researchers to apply null-hypothesis significance testing (NHST) on their experimental results in order to establish the superiority of a classifier. However, such a frequentist approach has a number of inherent deficiencies and limitations, e.g., the inability to accept the null hypothesis (that the two classifiers perform equally well), the difficulty to compare commonly-used multivariate performance measures like F1 scores instead of accuracy, and so on. In this paper, we propose a novel Bayesian approach to the performance comparison of text classifiers, and argue its advantages over the traditional frequentist approach based on t-test etc. In contrast to the existing probabilistic model for F1 scores which is unpaired, our proposed model takes the correlation between classifiers into account and thus achieves greater statistical power. Using several typical text classification algorithms and a benchmark dataset, we demonstrate that the our approach provides rich information about the difference between two classifiers' performances

The noncommutative Lorentzian cylinder as an isospectral deformation

We present a new example of a finite-dimensional noncommutative manifold, namely the noncommutative cylinder. It is obtained by isospectral deformation of the canonical triple associated to the Euclidean cylinder. We discuss Connes' character formula for the cylinder. In the second part, we discuss noncommutative Lorentzian manifolds. Here, the definition of spectral triples involves Krein spaces and operators on Krein spaces. A central role is played by the admissible fundamental symmetries on the Krein space of square integrable sections of a spin bundle over a Lorentzian manifold. Finally, we discuss isospectral deformation of the Lorentzian cylinder and determine all admissible fundamental symmetries of the noncommutative cylinder.Comment: 30 page