检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

292 篇 会议
143 篇 期刊文献
3 册 图书

馆藏范围

438 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

294 篇 工学
- 218 篇 计算机科学与技术...
- 188 篇 软件工程
- 80 篇 信息与通信工程
- 32 篇 生物工程
- 21 篇 控制科学与工程
- 17 篇 仪器科学与技术
- 16 篇 生物医学工程（可授...
- 15 篇 化学工程与技术
- 13 篇 电子科学与技术（可...
- 9 篇 机械工程
- 8 篇 电气工程
- 6 篇 光学工程
- 5 篇 材料科学与工程（可...
- 4 篇 动力工程及工程热...
169 篇 理学
- 95 篇 物理学
- 68 篇 数学
- 38 篇 统计学（可授理学、...
- 37 篇 生物学
- 15 篇 化学
- 13 篇 系统科学
72 篇 管理学
- 51 篇 图书情报与档案管...
- 21 篇 管理科学与工程(可...
- 7 篇 工商管理
12 篇 医学
- 11 篇 临床医学
- 8 篇 基础医学(可授医学...
- 7 篇 药学(可授医学、理...
10 篇 文学
- 8 篇 外国语言文学
- 7 篇 中国语言文学
9 篇 法学
- 8 篇 社会学
7 篇 农学
- 5 篇 作物学
2 篇 经济学
2 篇 教育学
1 篇 军事学
1 篇 艺术学

主题

49 篇 speech recogniti...
24 篇 speech
21 篇 hidden markov mo...
21 篇 training
19 篇 speech processin...
14 篇 acoustics
13 篇 decoding
13 篇 natural language...
12 篇 computational mo...
11 篇 signal processin...
9 篇 computational li...
9 篇 databases
9 篇 feature extracti...
8 篇 natural language...
8 篇 syntactics
8 篇 automatic speech...
7 篇 training data
7 篇 testing
7 篇 speaker recognit...
6 篇 machine translat...

机构

27 篇 department of co...
24 篇 center for langu...
21 篇 department of co...
18 篇 department of co...
16 篇 mainlp center fo...
12 篇 munich
11 篇 department of co...
9 篇 center for infor...
9 篇 department of co...
9 篇 center for speec...
7 篇 department of co...
7 篇 human language t...
7 篇 department of el...
7 篇 center for langu...
7 篇 center for langu...
6 篇 center for speec...
6 篇 center for infor...
6 篇 speechlab depart...
6 篇 center for langu...
6 篇 national enginee...

作者

21 篇 plank barbara
18 篇 zheng thomas fan...
18 篇 yarowsky david
17 篇 thomas fang zhen...
15 篇 van der goot rob
14 篇 khudanpur sanjee...
12 篇 wang dong
12 篇 sanjeev khudanpu...
11 篇 callison-burch c...
11 篇 eisner jason
9 篇 schütze hinrich
9 篇 lei xie
9 篇 koehn philipp
9 篇 cotterell ryan
8 篇 du xiaojiang
8 篇 smith noah a.
8 篇 zhu liehuang
8 篇 watanabe shinji
7 篇 li zhifei
7 篇 dredze mark

语言

424 篇 英文
11 篇 其他
5 篇 中文

检索条件"机构=Department of Computer Science and Center for Language and Speech Processing"

共 438 条记录，以下是241-250 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

FrameNet+: Fast paraphrastic tripling of FrameNet 53

FrameNet+: Fast paraphrastic tripling of FrameNet

引用

53rd Annual Meeting of the Association for Computational Linguistics and 7th International Joint Conference on Natural language processing of the Asian Federation of Natural language processing, ACL-IJCNLP 2015

作者： Pavlick, Ellie Wolfe, Travis Rastogi, Pushpendre Callison-Burch, Chris Dredze, Mark Van Durme, Benjamin Computer and Information Science Department University of Pennsylvania United States Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781510808461

We increase the lexical coverage of FrameNet through automatic paraphrasing. We use crowdsourcing to manually filter out bad paraphrases in order to ensure a high-precision resource. Our expanded FrameNet contains an additional 22K lexical units, a 3-fold increase over the current FrameNet, and achieves 40% better coverage when evaluated in a practical setting on New York Times data. © 2015 Association for Computational Linguistics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Uncertainty estimation of DNN classifiers

Uncertainty estimation of DNN classifiers

引用

IEEE Workshop on Automatic speech Recognition and Understanding, ASRU 2015

作者： Mallidi, Sri Harish Ogawa, Tetsuji Hermansky, Hynek Center for Language and Speech Processing Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States Department of Computer Science Waseda University Tokyo Japan

ISBN: (纸本)9781479972913

New efficient measures for estimating uncertainty of deep neural network (DNN) classifiers are proposed and successfully applied to multistream-based unsupervised adaptation of ASR systems to address uncertainty derived from noise. The proposed measure is the error from associative memory models trained on outputs of a DNN. In the present study, an attempt is made to use autoencoders for remembering the property of data. Another measure proposed is an extension of the M-measure, which computes the divergences of probability estimates spaced at specific time intervals. The extended measure results in an improved reliability by considering the latent information of phoneme duration. Experimental comparisons carried out in a multistream-based ASR paradigm demonstrates that the proposed measures yielded improvements over the multistyle trained system and system selected based on existing measures. Fusion of the proposed measures achieved almost the same performance as the oracle system selection. © 2015 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing participant selection through caching in mobile crowd sensing

Enhancing participant selection through caching in mobile cr...

引用

International Workshop on Quality of Service

作者： Hanshang Li Ting Li Fan Li Weichao Wang Yu Wang Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China School of Computer Science Beijing Institute of Technology Beijing China

With the rapid increasing of smart phones and their embedded sensing technologies, mobile crowd sensing (MCS) becomes an emerging sensing paradigm for performing large-scale sensing tasks. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select the minimum set of participants from the huge user pool to perform the tasks and achieve certain level of coverage. In this paper, we introduce a new MCS architecture which leverages the cached sensing data to fulfill partial sensing tasks in order to reduce the size of selected participant set. We present a newly designed participant selection algorithm with caching and evaluate it via extensive simulations with a real-world mobile dataset.

关键词： Sensors Mobile communication Memory Algorithm design and analysis Smart phones Probabilistic logic Data models

来源：评论

学校读者我要写书评

暂无评论

EchoLoc: Accurate Device-Free Hand Localization Using COTS Devices

EchoLoc: Accurate Device-Free Hand Localization Using COTS D...

引用

International Conference on Parallel processing (ICPP)

作者： Huijie Chen Fan Li Yu Wang School of Computer Science Beijing Institute of Technology Beijing Engineering Research Center for High Volume Language Information Processing and Cloud Computing Applications Beijing China Department of Computer Science College of Computing and Informatics University of North Carolina at Charlotte Charlotte NC USA

ISBN: (纸本)9781509028245

Hand tracking systems are becoming increasingly popular as a fundamental HCI approach. The trajectory of moving hand can be estimated through smoothing the position coordinates collected from continuous localization. Therefore, hand localization is a key component of any hand tracking systems. This paper presents EchoLoc, which locates the human hand by leveraging the speaker array in Commercial Off-The-Shelf (COTS) devices (i.e., a smart phone plugged with a stereo speaker). EchoLoc measures the distance from the hand to the speaker array via the Time Of Flight (TOF) of the chirp. The speaker array and hand yield a unique triangle, therefore, the hand can be localized with triangular geometry. We prototype EchoLoc on iOS as an application, and find it is capable of localization with the average resolution within five centimeters of 73% and three centimeters of 48%.

关键词： Parallel processing

来源：评论

学校读者我要写书评

暂无评论

Labeled morphological segmentation with semi-markov models 19

Labeled morphological segmentation with semi-markov models

引用

19th Conference on Computational Natural language Learning, CoNLL 2015

作者： Cotterell, Ryan Müller, Thomas Fraser, Alexander Schütze, Hinrich Department of Computer Science Johns Hopkins University United States Center for Information and Language Processing University of Munich Germany

ISBN: (纸本)9781941643778

We present labeled morphological segmentation—an alternative view of morphological processing that unifies several tasks. We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmentation system that, contrary to previous work, explicitly models morphotactics. We show improved performance on three tasks for all six languages: (i) morphological segmentation, (ii) stemming and (iii) morphological tag classification. For morphological segmentation our method shows absolute improvements of 2-6 points F1 over a strong baseline. © 2015 Association for Computational Linguistics.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Morphological word-embeddings

Morphological word-embeddings

引用

Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL HLT 2015

作者： Cotterell, Ryan Schütze, Hinrich Department of Computer Science Johns Hopkins University United States Center for Information and Language Processing University of Munich Germany

ISBN: (纸本)9781941643495

Linguistic similarity is multi-faceted. For instance, two words may be similar with respect to semantics, syntax, or morphology inter alia. Continuous word-embeddings have been shown to capture most of these shades of similarity to some degree. This work considers guiding word-embeddings with morphologically annotated data, a form of semi-supervised learning, encouraging the vectors to encode a word's morphology, i.e., words close in the embedded space share morphological features. We extend the log-bilinear model to this end and show that indeed our learned embeddings achieve this, using German as a case study. © 2015 Association for Computational Linguistics.

关键词： Vector spaces

来源：评论

学校读者我要写书评

暂无评论

Joint lemmatization and morphological tagging with LEMMING

Joint lemmatization and morphological tagging with LEMMING

引用

Conference on Empirical Methods in Natural language processing, EMNLP 2015

作者： Müller, Thomas Cotterell, Ryan Fraser, Alexander Schütze, Hinrich Center for Information and Language Processing University of Munich Germany Department of Computer Science Johns Hopkins University United States

ISBN: (纸本)9781941643327

We present Lemming, a modular loglinear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or analyzers. Lemming sets the new state of the art in token-based statistical lemmatization on six languages;e.g., for Czech lemmatization, we reduce the error by 60%, from 4.05 to 1.58. We also give empirical evidence that jointly modeling morphological tags and lemmata is mutually beneficial. © 2015 Association for Computational Linguistics.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Unsupervised speaker adaptation of deep neural network based on the combination of speaker codes and singular value decomposition for speech recognition 40

Unsupervised speaker adaptation of deep neural network based...

引用

40th IEEE International Conference on Acoustics, speech, and Signal processing, ICASSP 2015

作者： Xue, Shaofei Jiang, Hui Dai, Lirong Liu, Qingfeng National Engineering Laboratory of Speech and Language Information Processing University of Science and Technology of China Hefei China Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (纸本)9781467369978

Recently, we have proposed a general adaptation scheme for deep neural network based on discriminant condition codes and applied it to supervised speaker adaptation in speech recognition based on either frame-level cross-entropy or sequence-level maximum mutual information training criterion [1, 2, 3, 4]. In this case, each condition code is associated with one speaker in data, which is thus called speaker code for convenience. Our previous work has shown that speaker code based methods are quite effective in adapting DNNs even when only a very small amount of adaptation data is available. However, we have to use a large speaker code size and complex processes to obtain the best ASR performance since good initializations of speaker codes and connection weights are very important. In this paper, we propose a method using singular value decomposition (SVD) as in [5] to initialize speaker codes and connection weights to obtain a comparable ASR performance as before but with a smaller speaker code size and much less computation complexity. Meanwhile, we have evaluated unsupervised speaker adaptation with the proposed method in large vocabulary speech recognition in the Switchboard task. Experimental results have shown that it is effective for providing well initializations and suitable in adapting large DNN models. © 2015 IEEE.

关键词： Deep Neural Network (DNN) singular value decomposition (SVD) Speaker Adaptation Speaker Code

来源：评论

学校读者我要写书评

暂无评论

LCCT: A semi-supervised model for sentiment classification

LCCT: A semi-supervised model for sentiment classification

引用

Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL HLT 2015

作者： Yang, Min Tu, Wenting Lu, Ziyu Yin, Wenpeng Chow, Kam-Pui Department of Computer Science University of Hong Kong Hong Kong Hong Kong Center for Information and Language Processing University of Munich Germany

ISBN: (纸本)9781941643495

Analyzing public opinions towards products, services and social events is an important but challenging task. An accurate sentiment analyzer should take both lexicon-level information and corpus-level information into account. It also needs to exploit the domainspecific knowledge and utilize the common knowledge shared across domains. In addition, we want the algorithm being able to deal with missing labels and learning from incomplete sentiment lexicons. This paper presents a LCCT (Lexicon-based and Corpus-based, Co-Training) model for semi-supervised sentiment classification. The proposed method combines the idea of lexicon-based learning and corpus-based learning in a unified cotraining framework. It is capable of incorporating both domain-specific and domainindependent knowledge. Extensive experiments show that it achieves very competitive classification accuracy, even with a small portion of labeled data. Comparing to state-ofthe-art sentiment classification methods, the LCCT approach exhibits significantly better performances on a variety of datasets in both English and Chinese. © 2015 Association for Computational Linguistics.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Uncertainty estimation of DNN classifiers

Uncertainty estimation of DNN classifiers

引用

IEEE Workshop on Automatic speech Recognition and Understanding

作者： Sri Harish Mallidi Tetsuji Ogawa Hynek Hermansky Center for Language and Speech Processing The Johns Hopkins University Baltimore U.S.A Department of Computer Science Waseda University Tokyo Japan Human Language Technology Center of Excellence The Johns Hopkins University Baltimore U.S.A

关键词： Uncertainty Measurement uncertainty speech Noise measurement Training Training data Estimation

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共44页 << < 21 22 23 24 25 26 27 28 29 30 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：