检索结果-内蒙古大学图书馆

2013 AAAI Fall Symposium

作者： Lawrie, Dawn Finin, Tim Mayfield, James McNamee, Paul Computer Science Department Loyola University Maryland Baltimore MD United States University of Maryland Baltimore County Baltimore MD United States Johns Hopkins University Human Language Technology Center of Excellence Baltimore MD United States

ISBN: (纸本)9781577356424

One way to obtain large amounts of semantic data is to extract facts from the vast quantities of text that is now available on-line. The relatively low accuracy of current information extraction techniques introduces a need for evaluating the quality of the knowledge bases (KBs) they generate. We frame the problem as comparing KBs generated by different systems from the same documents and show that exploiting provenance leads to more efficient techniques for aligning them and identifying their differences. We describe two types of tools: entity-match focuses on differences in entitles found and linked;kbdiff focuses on differences in relations among those entities. Together, these tools support assessment of relative KB accuracy by sampling the parts of two KBs that disagree. We explore the usefulness of the tools through the construction of tens of different KBs built from the same 26,000 Washington Post articles and identifying the differences. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

What affects patient (dis)satisfaction? Analyzing online doctor ratings with a joint topic-sentiment model

What affects patient (dis)satisfaction? Analyzing online doc...

引用

2013 AAAI Workshop

作者： Paul, Michael J. Wallace, Byron C. Dredze, Mark Dept. of Computer Science Johns Hopkins University Baltimore MD 21218 United States Center for Evidence-based Medicine Brown University Providence RI 02903 United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21211 United States

ISBN: (纸本)9781577356202

We analyze patient reviews of doctors using a novel probabilistic joint model of topic and sentiment based on factorial LDA (Paul and Dredze 2012). We leverage this model to exploit a small set of previously annotated reviews to automatically analyze the topics and sentiment latent in over 50,000 online reviews of physicians (and we make this dataset publicly available). The proposed model outperforms baseline models for this task with respect to model perplexity and sentiment classification. We report the most representative words with respect to positive and negative sentiment along three clinical aspects, thus complementing existing qualitative work exploring patient reviews of physicians. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

COMPLEMENTARY ENVELOPE ESTIMATION FOR FREQUENCY-MODULATED RANDOM SIGNALS

COMPLEMENTARY ENVELOPE ESTIMATION FOR FREQUENCY-MODULATED RA...

引用

IEEE International Conference on Acoustics, speech and Signal processing

作者： Pascal Clark Ivars Kirsteins Les Atlas Johns Hopkins University Human Language Technology Center of Excellence Naval Undersea Warfare Center Newport Department of Electrical Engineering University of Washington

ISBN: (纸本)9781479903573

The Hilbert envelope is a well-known indicator for amplitude modulation in subband time series. However, frequency modulation estimators typically impose temporal smoothness constraints that limit their general use for stochastic signals. We introduce the complementary envelope as a direct indicator of stochastic phase coherence and frequency modulation. Rooted in the second-order statistics of randomly-phased sinusoids, the complementary envelope is distinct from the Hilbert envelope, and can be estimated easily and separately. We propose a new complementary envelope estimator based on modified multitaper spectral analysis, and use it to reveal previously unseen FM-like behavior in ship propeller noise.

关键词： Modulation Complementary statistics Impropriety Multitaper Time-frequency

来源：评论

学校读者我要写书评

暂无评论

SUPERVECTOR BAYESIAN SPEAKER COMPARISON

SUPERVECTOR BAYESIAN SPEAKER COMPARISON

引用

IEEE International Conference on Acoustics, speech and Signal processing

作者： Bengt J. Borgstrom Alan McCree MIT Lincoln Laboratory Lexington MA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD

ISBN: (纸本)9781479903573

In this paper we propose fully Bayesian speaker comparison of supervectors, which we refer to as SV-BSC, as a method for estimating whether a test cut was generated by the same speaker as an enrollment set. We derive the SV-BSC log-likelihood ratio of same-speaker to different-speaker hypotheses, and present solutions for model training and Bayesian scoring. We then show that if speaker and channel variability are assumed to inhabit a total variability sub-space, SV-BSC scoring reduces to a form which requires only low-computation subspace operations. Finally, we show that common speaker recognition techniques such as Joint Factor Analysis (JFA) and i-vector Probabilistic Linear Discriminant Analysis (PLDA) are approximations to this full solution under certain additional assumptions. Experiments on the NIST 2010 SRE show SV-BSC to outperform a PLDA system.

关键词： Loudspeakers test cutting speaker recognition Subspace joint factor Model trains Scoring Bayesian inference

来源：评论

学校读者我要写书评

暂无评论

Joshua 4.0: Packing, PRO, and Paraphrases 7

Joshua 4.0: Packing, PRO, and Paraphrases

引用

7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - human language Technologies, NAACL HLT 2012

作者： Ganitkevitch, Juri Cao, Yuan Weese, Jonathan Post, Matt Callison-Burch, Chris Center For Language And Speech Processing Human Language Technology Center Of Excellence Johns Hopkins University United States

ISBN: (纸本)9781937284206

We present Joshua 4.0, the newest version of our open-source decoder for parsing-based statistical machine translation. The main contributions in this release are the introduction of a compact grammar representation based on packed tries, and the integration of our implementation of pairwise ranking optimization, J-PRO. We further present the extension of the Thrax SCFG grammar extractor to pivot-based extraction of syntactically informed sentential paraphrases. ©2012 Association for Computational Linguistics.

关键词： Machine translation

来源：评论

学校读者我要写书评

暂无评论

Phonetically balanced and psychometrically equivalent monosyllabic word lists for word recognition testing in Thai

引用

Proceedings of Meetings on Acoustics 2015年第1期25卷

作者： Sajeerat Poonyaban Pasinee Aungsakulchai Charturong Tantibundhit Chutamanee Onsuwan Rattinan Tiravanitchakul Krit Kosawat Adirek Munthuli 1Center of Excellence in Intelligent Informatics Speech and Language Technology and Service Innovation (CILS) Thammasat University Thailand 2Department of Electrical and Computer Engineering Faculty of Engineering Thammasat University Rangsit Campus Khlong Luang Pathum Thani Thailand pond_poonyaban@*** meilyp.aung@*** tchartur@engr.tu.ac.th ***@student.tu.ac.th 3Department of English and Linguistics Faculty of Liberral Arts Thammasat University Rangsit Campus Khlong Luang Pathum Thani Thailand consuwan@tu.ac.th 4Department of Communication Science and Disorders Faculty of Medicine Mahidol University Thailand rartv@*** 5NECTEC National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand krit.kosawat@nectec.or.th

Word recognition testing may be defined as a procedure to assess a listener’s ability to identify one-syllable words (such as phonetically-balanced/PB words) that are presented at a given suprathreshold level to arrive at a word recognition score. For Thai, Thammasat University and Ramathibodi Hospital Phonetically Balanced Word Lists 2015 (TU-RAMA PB’15) were created with five lists, each with 25 monosyllabic words. Besides its phoneme distributions being based on large-scale Thai spoken corpora, TU-RAMA PB’15 is in line with TU PB’14 with emphasis on phonetic balance, symmetrical phoneme occurrence, and word familiarity. To evaluate its homogeneity in terms of decibel intelligibility, the lists were recorded and presented to 10 normal hearing participants, ranging from 0 to 50 dB HL in 2 dB increments (ascending order) until they repeated correct verbal responses. Using logistic regression, regression slopes and intercepts were calculated to estimate percentage of correct performance at any given intensity and to construct psychometric functions for every list. Derived psychometric function slopes ranged from 0.2015 to 0.2262 while intensities required for 50% intelligibility ranged from 17.0876 to 20.8856. Two-way Chi-Square analysis performed on both parameters indicated that there was no significant difference among the five lists.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Shared components topic models

Shared components topic models

引用

2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2012

作者： Gormley, Matthew R. Dredze, Mark Van Durme, Benjamin Eisner, Jason Center for Language and Speech Processing Human Language Technology Center of Excellence Department of Computer Science BaltimoreMD United States

ISBN: (纸本)1937284204

With a few exceptions, extensions to latent Dirichlet allocation (LDA) have focused on the distribution over topics for each document. Much less attention has been given to the underlying structure of the topics themselves. As a result, most topic models generate topics independently from a single underlying distribution and require millions of parameters, in the form of multinomial distributions over the vocabulary. In this paper, we introduce the Shared Components Topic Model (SCTM), in which each topic is a normalized product of a smaller number of underlying component distributions. Our model learns these component distributions and the structure of how to combine subsets of them into topics. The SCTM can represent topics in a much more compact representation than LDA and achieves better perplexity with fewer parameters. © 2012 Association for Computational Linguistics.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

Revisiting the case for explicit syntactic information in language models

Revisiting the case for explicit syntactic information in la...

引用

2012 Workshop on Will We Ever Really Replace the N-gram Model? On the Future of language Modeling for HLT, WLM 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2012

作者： Rastrow, Ariya Khudanpur, Sanjeev Dredze, Mark Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University BaltimoreMD United States

Statistical language models used in deployed systems for speech recognition, machine translation and other human language technologies are almost exclusively n-gram models. They are regarded as linguistically naïve, but estimating them from any amount of text, large or small, is straightforward. Furthermore, they have doggedly matched or outperformed numerous competing proposals for syntactically well-motivated models. This unusual resilience of n-grams, as well as their weaknesses, are examined here. It is demonstrated that n-grams are good word-predictors, even linguistically speaking, in a large majority of word-positions, and it is suggested that to improve over n-grams, one must explore syntax-aware (or other) language models that focus on positions where n-grams are weak. © 2012 Association for Computational Linguistics.

关键词： speech recognition

来源：评论

学校读者我要写书评

暂无评论

Discriminatively trained Bayesian speaker comparison of i-vectors

Discriminatively trained Bayesian speaker comparison of i-ve...

引用

International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Bengt J. Borgström Alan McCree Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479903573

This paper presents a framework for fully Bayesian speaker comparison of i-vectors. By generalizing the train/test paradigm, we derive an analytic expression for the speaker comparison log-likelihood ratio (LLR), as well as solutions for model training and Bayesian scoring. This framework is useful for enrollment sets of any size. For the specific case of single-cut enrollment, it is shown to be mathematically equivalent to probabilistic linear discriminant analysis (PLDA). Additionally, we present discriminative training of model hyper-parameters by minimizing the total cross entropy between LLRs and class labels. When applied to speaker recognition, significant performance gains are observed for various NIST SRE 2010 extended evaluation tasks.

关键词： Training Bayes methods Speaker recognition Covariance matrices Entropy NIST Linear programming

来源：评论

学校读者我要写书评

暂无评论

Experimenting with drugs (and topic models): Multi-dimensional exploration of recreational drug discussions

Experimenting with drugs (and topic models): Multi-dimension...

引用

2012 AAAI Fall Symposium

作者： Paul, Michael J. Dredze, Mark Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21218 United States

Clinical research of new recreational drugs and trends requires mining current information from non-traditional text sources. In this work we support such research through the use of a multi-dimensional latent text model - factorial LDA - that captures orthogonal factors of corpora, creating structured output for researchers to better understand the contents of a corpus. Since a purely unsupervised model is unlikely to discover specific factors of interest to clinical researchers, we modify the structure of factorial LDA to incorporate prior knowledge, including the use of of observed variables, informative priors and background components. The resulting model learns factors that correspond to drug type, delivery method (smoking, injection, etc.), and aspect (chemistry, culture, effects, health, usage). We demonstrate that the improved model yields better quantitative and more interpretable results. Copyright © 2012, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：