On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

Leximancer concept mapping of patient case studies

Quantitative databases are limited to information identified as important by their creators, while databases containing natural language are limited by our ability to analyze large unstructured bodies of text. Leximancer is a tool that uses semantic mapping to develop concept maps from natural language. We have applied Leximancer to educational based pathology case notes to demonstrate how real patient records or databases of case studies could be analyzed to identify unique relationships. We then discuss how such analysis could be used to conduct quantitative analysis from databases such as the Coronary Heart Disease Database.

Perceptions of kanji learning strategies: Do they differ among Chinese character and alphabetic background learners

This study investigates three important issues in kanji learning strategies; namely, strategy use, effectiveness of strategy and orthographic background. A questionnaire on kanji learning strategy use and perceived effectiveness was administered to 116 beginner level, undergraduate students of Japanese from alphabetic and character backgrounds in Australia. Both descriptive and statistical analyses of the questionnaire responses revealed that the strategies used most often are the most helpful. Repeated writing was reported as the most used strategy type although alphabetic background learners reported using repeated writing strategies significantly more often than character background learners. The importance of strategy training and explicit instruction of fundamental differences between character and alphabetic background learners of Japanese is discussed in relation to teaching strategies. [Author abstract]

Extreme learning machine for predicting HLA-peptide binding

Machine learning techniques have been recognized as powerful tools for learning from data. One of the most popular learning techniques, the Back-Propagation (BP) Artificial Neural Networks, can be used as a computer model to predict peptides binding to the Human Leukocyte Antigens (HLA). The major advantage of computational screening is that it reduces the number of wet-lab experiments that need to be performed, significantly reducing the cost and time. A recently developed method, Extreme Learning Machine (ELM), which has superior properties over BP has been investigated to accomplish such tasks. In our work, we found that the ELM is as good as, if not better than, the BP in term of time complexity, accuracy deviations across experiments, and most importantly - prevention from over-fitting for prediction of peptide binding to HLA.

Wills' mineral processing technology: an introduction to the practical aspects of ore treatment and mineral recovery

"Wills' Mineral Processing Technology" provides practising engineers and students of mineral processing, metallurgy and mining with a review of all of the common ore-processing techniques utilized in modern processing installations. Now in its Seventh Edition, this renowned book is a standard reference for the mineral processing industry. Chapters deal with each of the major processing techniques, and coverage includes the latest technical developments in the processing of increasingly complex refractory ores, new equipment and process routes. This new edition has been prepared by the prestigious J K Minerals Research Centre of Australia, which contributes its world-class expertise and ensures that this will continue to be the book of choice for professionals and students in this field. This latest edition highlights the developments and the challenges facing the mineral processor, particularly with regard to the environmental problems posed in improving the efficiency of the existing processes and also in dealing with the waste created. The work is fully indexed and referenced. -The classic mineral processing text, revised and updated by a prestigious new team -Provides a clear exposition of the principles and practice of mineral processing, with examples taken from practice -Covers the latest technological developments and highlights the challenges facing the mineral processor -New sections on environmental problems, improving the efficiency of existing processes and dealing with waste.

Authorship Attribution with Support Vector Machines

In this paper we explore the use of text-mining methods for the identification of the author of a text. We apply the support vector machine (SVM) to this problem, as it is able to cope with half a million of inputs it requires no feature selection and can process the frequency vector of all words of a text. We performed a number of experiments with texts from a German newspaper. With nearly perfect reliability the SVM was able to reject other authors and detected the target author in 60–80% of the cases. In a second experiment, we ignored nouns, verbs and adjectives and replaced them by grammatical tags and bigrams. This resulted in slightly reduced performance. Author detection with SVMs on full word forms was remarkably robust even if the author wrote about different topics.

Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster

Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.

The Coefficient of Variance as an index of L2 lexical processing skill

The Coefficient of Variance (mean standard deviation/mean Response time) is a measure of response time variability that corrects for differences in mean Response time (RT) (Segalowitz & Segalowitz, 1993). A positive correlation between decreasing mean RTs and CVs (rCV-RT) has been proposed as an indicator of L2 automaticity and more generally as an index of processing efficiency. The current study evaluates this claim by examining lexical decision performance by individuals from three levels of English proficiency (Intermediate ESL, Advanced ESL and L1 controls) on stimuli from four levels of item familiarity, as defined by frequency of occurrence. A three-phase model of skill development defined by changing rCV-RT.values was tested. Results showed that RTs and CVs systematically decreased as a function of increasing proficiency and frequency levels, with the rCV-RT serving as a stable indicator of individual differences in lexical decision performance. The rCV-RT and automaticity/restructuring account is discussed in light of the findings. The CV is also evaluated as a more general quantitative index of processing efficiency in the L2.

Subcortical brain mechanisms in speech and language

This paper reviews current research and contemporary theories of subcortical participation in the motor control of speech production and language processing. As a necessary precursor to the discussion of the functional roles of the basal ganglia and thalamus, the neuroanatomy of the basal ganglial-thalamocortical circuitry is described. Contemporary models of hypokinetic and hyperkinetic movement disorders based on recent neuroanatomical descriptions of the multi-segmented circuits that characterise basal ganglion anatomy are described. Reported effects of surgically induced lesions in the globus pallidus and thalamus on speech production are reviewed. In addition, contemporary models proposed to explain the possible contribution of various subcortical structures to language processing are described and discussed in the context of evidence gained from observation of the effects of circumscribed surgically induced lesions in the basal ganglia and thalamus on language function. The potential of studies based on examination of the speech/language outcomes of patients undergoing pallidotomy and thalamotomy to further inform the debate relating to the role of subcortical structures in speech motor control and language processing is highlighted. Copyright (C) 2001 S. Karger AG, Basel.