9,362 research outputs found

    Taking on new challenges in multi-word unit processing for Machine Translation

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    This paper discusses the qualitative comparative evaluation performed on the results of two machine translation systems with different approaches to the processing of multi-word units. It proposes a solution for overcoming the difficulties multi-word units present to machine translation by adopting a methodology that combines the lexicon grammar approach with OpenLogos ontology and semantico-syntactic rules. The paper also discusses the importance of a qualitative evaluation metrics to correctly evaluate the performance of machine translation engines with regards to multi-word units

    An intelligent computer- based tutoring approach for the management of negative transfer

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    This research addresses how a prototype of a language tutoring system, the Chinese Tutor, tackles the practical problem of negative transfer (i.e. mother tongue influence) in the learning of Chinese grammar by English-speaking students. The design of the Chinese Tutor has been based on the results of empirical studies carried out as part of this research. The results of the data analysis show that negative transfer can be used to account for almost 80% of the errors observed in the linguistic output of students in their study of Chinese. If the students can be helped to overcome these errors, the standard of their Chinese will be greatly improved. In this research, an approach of Intelligent Language Tutoring Systems (ILTSs) has been adopted for handling negative transfer. This is because there are several advantages of ILTSs, including interactive learning, highly individualised instruction and student-centred instruction [Wyatt 1984 .The Chinese Tutor contains five main components: the Expert Model, which contains all the linguistic knowledge for tutoring and serves as a standard for evaluating the student's performance; the Student Model, which collects information on the student's performance; the Diagnoser, which detects different types of error made by the student; the Tutor Model, which plans student learning, makes didactic decisions and chooses an appropriate tutorial strategy based on the student’s performance; and the Interface Module, which communicates between the student and the system. A general and robust solution to the treatment of negative transfer, i.e. the technique of Mixed Grammar has been devised. The rules in this grammar can be applied to detect arbitrary transfer errors by using a general set of rules. A number of students in the Department of East Asian Studies at the University of Durham have used the Chinese Tutor with positive results

    Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

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    This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

    Assessing the Accuracy of Discourse Connective Translations: Validation of an Automatic Metric

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    Automatic metrics for the evaluation of machine translation (MT) compute scores that characterize globally certain aspects of MT quality such as adequacy and fluency. This paper introduces a reference-based metric that is focused on a particular class of function words, namely discourse connectives, of particular importance for text structuring, and rather challenging for MT. To measure the accuracy of connective translation (ACT), the metric relies on automatic word-level alignment between a source sentence and respectively the reference and candidate translations, along with other heuristics for comparing translations of discourse connectives. Using a dictionary of equivalents, the translations are scored automatically, or, for better precision, semi-automatically. The precision of the ACT metric is assessed by human judges on sample data for English/French and English/Arabic translations: the ACT scores are on average within 2% of human scores. The ACT metric is then applied to several commercial and research MT systems, providing an assessment of their performance on discourse connectives

    A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

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    This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

    Psychometrics in Practice at RCEC

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    A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment
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