咨询与建议

限定检索结果

文献类型

  • 542 篇 会议
  • 393 篇 期刊文献
  • 4 册 图书

馆藏范围

  • 939 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 594 篇 工学
    • 434 篇 计算机科学与技术...
    • 372 篇 软件工程
    • 134 篇 信息与通信工程
    • 66 篇 控制科学与工程
    • 54 篇 生物工程
    • 41 篇 生物医学工程(可授...
    • 38 篇 电气工程
    • 31 篇 机械工程
    • 25 篇 电子科学与技术(可...
    • 22 篇 化学工程与技术
    • 16 篇 光学工程
    • 14 篇 动力工程及工程热...
  • 368 篇 理学
    • 167 篇 物理学
    • 135 篇 数学
    • 89 篇 生物学
    • 65 篇 统计学(可授理学、...
    • 44 篇 系统科学
    • 26 篇 化学
  • 133 篇 管理学
    • 75 篇 图书情报与档案管...
    • 53 篇 管理科学与工程(可...
    • 30 篇 工商管理
  • 78 篇 医学
    • 67 篇 临床医学
    • 48 篇 基础医学(可授医学...
    • 24 篇 公共卫生与预防医...
    • 21 篇 药学(可授医学、理...
  • 53 篇 教育学
    • 38 篇 教育学
    • 26 篇 心理学(可授教育学...
  • 45 篇 法学
    • 43 篇 社会学
  • 21 篇 文学
    • 14 篇 外国语言文学
  • 17 篇 农学
  • 13 篇 经济学
    • 13 篇 应用经济学
  • 2 篇 哲学
  • 2 篇 军事学
  • 1 篇 历史学
  • 1 篇 艺术学

主题

  • 80 篇 speech recogniti...
  • 32 篇 hidden markov mo...
  • 27 篇 training
  • 24 篇 speech
  • 21 篇 semantics
  • 21 篇 machine learning
  • 21 篇 humans
  • 20 篇 computer science
  • 20 篇 machine translat...
  • 20 篇 decoding
  • 18 篇 feature extracti...
  • 16 篇 transducers
  • 15 篇 deep learning
  • 14 篇 computer aided l...
  • 14 篇 neural machine t...
  • 13 篇 natural language...
  • 13 篇 natural language...
  • 12 篇 support vector m...
  • 12 篇 computational li...
  • 11 篇 students

机构

  • 44 篇 apptek gmbh aach...
  • 36 篇 human language t...
  • 29 篇 human language t...
  • 18 篇 human language t...
  • 17 篇 institute of res...
  • 16 篇 human language t...
  • 15 篇 trustworthy huma...
  • 13 篇 department of in...
  • 12 篇 department of la...
  • 11 篇 department of co...
  • 11 篇 computer science...
  • 10 篇 human language t...
  • 9 篇 department of co...
  • 9 篇 human language t...
  • 9 篇 hkust human lang...
  • 9 篇 center for speec...
  • 9 篇 future technolog...
  • 9 篇 school of medici...
  • 8 篇 julius centre fo...
  • 8 篇 computer science...

作者

  • 139 篇 ney hermann
  • 63 篇 schlüter ralf
  • 34 篇 wu dekai
  • 33 篇 hermann ney
  • 22 篇 habernal ivan
  • 18 篇 dredze mark
  • 18 篇 hosseinzadeh meh...
  • 17 篇 ralf schlüter
  • 17 篇 pascale fung
  • 17 篇 zhou wei
  • 16 篇 rahmani amir mas...
  • 15 篇 fung pascale
  • 14 篇 gao yingbo
  • 14 篇 michel wilfried
  • 13 篇 ralf schluter
  • 12 篇 yousefpoor moham...
  • 12 篇 zeineldeen moham...
  • 12 篇 thomas fang zhen...
  • 12 篇 badriyya b.al-on...
  • 12 篇 zeyer albert

语言

  • 924 篇 英文
  • 11 篇 其他
  • 4 篇 中文
检索条件"机构=Department of Computer Science Department of Language and Human Development"
939 条 记 录,以下是51-60 订阅
排序:
Beam search for solving substitution ciphers
Beam search for solving substitution ciphers
收藏 引用
51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
作者: Nuhn, Malte Schamper, Julian Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
In this paper we address the problem of solving substitution ciphers using a beam search approach. We present a conceptually consistent and easy to implement method that improves the current state of the art for decip... 详细信息
来源: 评论
Modeling Punctuation Prediction as Machine Translation  8
Modeling Punctuation Prediction as Machine Translation
收藏 引用
8th International Workshop on Spoken language Translation, IWSLT 2011
作者: Peitz, Stephan Freitag, Markus Mauser, Arne Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
Punctuation prediction is an important task in Spoken language Translation. The output of speech recognition systems does not typically contain punctuation marks. In this paper we analyze different methods for punctua... 详细信息
来源: 评论
Transformer-based direct hidden Markov model for machine translation  59
Transformer-based direct hidden Markov model for machine tra...
收藏 引用
2021 Student Research Workshop, SRW 2021 at the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural language Processing, ACL-IJCNLP 2021
作者: Wang, Weiyue Yang, Zijian Gao, Yingbo Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Germany
The neural hidden Markov model has been proposed as an alternative to attention mechanism in machine translation with recurrent neural networks. However, since the introduction of the transformer models, its performan... 详细信息
来源: 评论
UTD HLTRI at TREC 2018: Complex Answer Retrieval Track  27
UTD HLTRI at TREC 2018: Complex Answer Retrieval Track
收藏 引用
27th Text REtrieval Conference, TREC 2018
作者: Maldonado, Ramon Harabagiu, Sanda M. Department of Computer Science Human Language Technology Research Institute University of Texas Dallas United States
Finding answers to complex questions within a corpus of Wikipedia paragraphs needs to account for (a) the similarity between questions and paragraphs as well as (b) their shared semantics. In our participation in the ... 详细信息
来源: 评论
Phrase training based adaptation for statistical machine translation
Phrase training based adaptation for statistical machine tra...
收藏 引用
2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2013
作者: Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
We present a novel approach for translation model (TM) adaptation using phrase training. The proposed adaptation procedure is initialized with a standard general-domain TM, which is then used to perform phrase trainin... 详细信息
来源: 评论
Combining Translation and language Model Scoring for Domain-Specific Data Filtering  8
Combining Translation and Language Model Scoring for Domain-...
收藏 引用
8th International Workshop on Spoken language Translation, IWSLT 2011
作者: Mansour, Saab Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
The increasing popularity of statistical machine translation (SMT) systems is introducing new domains of translation that need to be tackled. As many resources are already available, domain adaptation methods can be a... 详细信息
来源: 评论
Phrase Training Based Adaptation for Statistical Machine Translation  2
Phrase Training Based Adaptation for Statistical Machine Tra...
收藏 引用
2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2013
作者: Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
We present a novel approach for translation model (TM) adaptation using phrase training. The proposed adaptation procedure is initialized with a standard general-domain TM, which is then used to perform phrase trainin... 详细信息
来源: 评论
Decipherment complexity in 1:1 substitution ciphers
Decipherment complexity in 1:1 substitution ciphers
收藏 引用
51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
作者: Nuhn, Malte Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
In this paper we show that even for the case of 1:1 substitution ciphers-which encipher plaintext symbols by exchanging them with a unique substitute-finding the optimal decipherment with respect to a bigram language ... 详细信息
来源: 评论
A Simple and Effective Weighted Phrase Extraction for Machine Translation Adaptation  9
A Simple and Effective Weighted Phrase Extraction for Machin...
收藏 引用
9th International Workshop on Spoken language Translation, IWSLT 2012
作者: Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
The task of domain-adaptation attempts to exploit data mainly drawn from one domain (e.g. news) to maximize the performance on the test domain (e.g. weblogs). In previous work, weighting the training instances was use... 详细信息
来源: 评论
Spoken language Translation Using Automatically Transcribed Text in Training  9
Spoken Language Translation Using Automatically Transcribed ...
收藏 引用
9th International Workshop on Spoken language Translation, IWSLT 2012
作者: Peitz, Stephan Wiesler, Simon Nußbaum-Thom, Markus Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
In spoken language translation a machine translation system takes speech as input and translates it into another language. A standard machine translation system is trained on written language data and expects written ... 详细信息
来源: 评论