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High-temperature behavior of SiC power diodes

High-temperature behavior of SiC power diodes

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European Conference on Power Electronics and Applications

作者： Cyril Buttay Christophe Raynaud Hervé Morel Mihai Lazar Gabriel Civrac Dominique Bergogne Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA College of Information Science and Engineering Ritsumeikan University Shiga Japan Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781612841670

Silicon Carbide devices are in theory able to operate at very high temperatures, but many mechanisms actually lower the limit. In this paper we describe two of these mechanisms: the thermal run-away, and the ageing of the device. Ageing effects are assessed through two different set-ups: SiC diodes in plastic packages are stored for long periods (up to 2000 hrs) in a furnace with a temperature ranging from 200 to 250°C, while bare die diodes are stored in vacuum at a temperature of 350°C. A study is then performed to assess whether the diodes under test, which have a MPS structure, are sensitive to thermal run-away. It is found that the mixed unipolar-bipolar architecture offers much more robustness than a pure Schottky Barrier Diode would.

关键词： Schottky diodes Silicon carbide Aging Resistance Current measurement Temperature sensors

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Unsupervised model adaptation using information-theoretic criterion

Unsupervised model adaptation using information-theoretic cr...

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"2010 human language Technologies Conference ofthe North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010"

作者： Rastrow, Ariya Jelinek, Frederick Sethy, Abhinav Ramabhadran, Bhuvana Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University United States IBM T.J. Watson Research Center Yorktown Heights NY United States

ISBN: (纸本)1932432655

In this paper we propose a novel general framework for unsupervised model adaptation. Our method is based on entropy which has been used previously as a regularizer in semi-supervised learning. This technique includes another term which measures the stability of posteriors w.r.t model parameters, in addition to conditional entropy. The idea is to use parameters which result in both low conditional entropy and also stable decision rules. As an application, we demonstrate how this framework can be used for adjusting language model interpolation weight for speech recognition task to adapt from Broadcast news data to MIT lecture data. We show how the new technique can obtain comparable performance to completely supervised estimation of interpolation parameters. © 2010 Association for Computational Linguistics.

关键词： speech recognition

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We're not in kansas anymore: Detecting domain changes in streams

We're not in kansas anymore: Detecting domain changes in str...

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Conference on Empirical Methods in Natural language processing, EMNLP 2010

作者： Dredze, Mark Oates, Tim Piatko, Christine Human Language Technology Center of Excellence United States Center for Language and Speech Processing United States Applied Physics Lab. Johns Hopkins University United States University of Maryland Baltimore County United States

ISBN: (纸本)1932432868

Domain adaptation, the problem of adapting a natural language processing system trained in one domain to perform well in a different domain, has received significant attention. This paper addresses an important problem for deployed systems that has received little attention - detecting when such adaptation is needed by a system operating in the wild, i.e., performing classification over a stream of unlabeled examples. Our method uses A-distance, a metric for detecting shifts in data streams, combined with classification margins to detect domain shifts. We empirically show effective domain shift detection on a variety of data sets and shift conditions. © 2010 Association for Computational Linguistics.

关键词： Natural language processing systems

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Streaming cross document entity coreference resolution

Streaming cross document entity coreference resolution

引用

23rd International Conference on Computational Linguistics, Coling 2010

作者： Rao, Delip McNamee, Paul Dredze, Mark Human Language Technology Center of Excellence Johns Hopkins University United States

Previous research in cross-document entity coreference has generally been restricted to the offline scenario where the set of documents is provided in advance. As a consequence, the dominant approach is based on greedy agglomerative clustering techniques that utilize pairwise vector comparisons and thus require O(n2) space and time. In this paper we explore identifying coreferent entity mentions across documents in high-volume streaming text, including methods for utilizing orthographic and contextual information. We test our methods using several corpora to quantitatively measure both the efficacy and scalability of our streaming approach. We show that our approach scales to at least an order of magnitude larger data than previous reported methods.

关键词： Vector spaces

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Non-expert correction of automatically generated relation annotations

Non-expert correction of automatically generated relation an...

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2010 Workshop on Creating speech and language Data with Amazon's Mechanical Turk, Mturk 2010 at the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2010

作者： Gormley, Matthew R. Gerber, Adam Harper, Mary Dredze, Mark Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21211 United States Laboratory for Computational Linguistics and Information Processing University of Maryland College ParkMD20742 United States

We explore a new way to collect human annotated relations in text using Amazon Mechanical Turk. Given a knowledge base of relations and a corpus, we identify sentences which mention both an entity and an attribute that have some relation in the knowledge base. Each noisy sentence/relation pair is presented to multiple turkers, who are asked whether the sentence expresses the relation. We describe a design which encourages user efficiency and aids discovery of cheating. We also present results on inter-annotator agreement. © 2010 Association for Computational Linguistics

关键词： Knowledge based systems

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A spoken term detection framework for recovering out-of-vocabulary words using the web

A spoken term detection framework for recovering out-of-voca...

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作者： Parada, Carolina Sethy, Abhinav Dredze, Mark Jelinek, Frederick Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University 3400 N Charles Street Baltimore MD 21210 United States IBM TJ Watson Research Center New York NY 10598 United States

Vocabulary restrictions in large vocabulary continuous speech recognition (LVCSR) systems mean that out-of-vocabulary (OOV) words are lost in the output. However, OOV words tend to be information rich terms (often named entities) and their omission from the transcript negatively affects both usability and downstream NLP technologies, such as machine translation or knowledge distillation. We propose a novel approach to OOV recovery that uses a spoken term detection (STD) framework. Given an identified OOV region in the LVCSR output, we recover the uttered OOVs by utilizing contextual information and the vast and constantly updated vocabulary on the Web. Discovered words are integrated into system output, recovering up to 40% of OOVs and resulting in a reduction in system error. © 2010 ISCA.

关键词： Recovery

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Model combination for speech recognition using empirical Bayes risk minimization

Model combination for speech recognition using empirical Bay...

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2010 IEEE Workshop on Spoken language technology, SLT 2010

作者： Deoras, Anoop Filimonov, Denis Harper, Mary Jelinek, Fred Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21218 United States UMIACS Laboratory for Computational Linguistics and Information Processing University of Maryland College Park MD 20742 United States

ISBN: (纸本)9781424479030

In this paper, we explore the model combination problem for rescoring Automatic speech Recognition (ASR) hypotheses. We use minimum Empirical Bayes Risk for the optimization criterion and Deterministic Annealing techniques to search through the non-convex parameter space. Our experiments on the DARPA WSJ task using several different language models showed that our approach consistently outperforms the standard methods of model combination that optimize using 1-best hypothesis error. ©2010 IEEE.

关键词： speech recognition

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Entity Disambiguation for Knowledge Base Population

Entity Disambiguation for Knowledge Base Population

引用

计算语言学第23届国际学术会议

作者： Mark Dredze Paul McNamee Delip Rao Adam Gerber Tim Finin Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University University of Maryland-Baltimore County

The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the *** is challenging due to issues such as non-uniform variations in entity names,mention ambiguity,and entities absent from a knowledge *** present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little ***,our approach achieves performance of up to 95%on entities mentioned from newswire and 80%on a public test set that was designed to include challenging queries.

关键词： Google Entity Disambiguation for Knowledge Base Population

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Improving semantic role labeling with word sense

Improving semantic role labeling with word sense

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"2010 human language Technologies Conference ofthe North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010"

作者： Che, Wanxiang Liu, Ting Li, Yongqiang Research Center for Information Retrieval MOE-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology 150001 China

ISBN: (纸本)1932432655

Semantic role labeling (SRL) not only needs lexical and syntactic information, but also needs word sense information. However, because of the lack of corpus annotated with both word senses and semantic roles, there is few research on using word sense for SRL. The release of OntoNotes provides an opportunity for us to study how to use word sense for SRL. In this paper, we present some novel word sense features for SRL and find that they can improve the performance significantly. © 2010 Association for Computational Linguistics.

关键词： Semantics

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Model combination for speech Recognition using Empirical Bayes Risk minimization

Model combination for Speech Recognition using Empirical Bay...

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IEEE Spoken language technology Workshop

作者： Anoop Deoras Denis Filimonov Mary Harper Fred Jelinek Center of Language and Speech Processing Johns Hopkins University Baltimore MD USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA UMIACS Laboratory of Computational Linguistics and Information Processing University of Maryland College Park MD USA Center of Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

关键词： speech recognition Annealing speech Optimization Training Decoding Cooling

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