作者: Bravura 時(shí)間: 2025-3-21 21:42 作者: Foreshadow 時(shí)間: 2025-3-22 03:00
Synthesis Lectures on Human Language Technologieshttp://image.papertrans.cn/l/image/586759.jpg作者: 無(wú)底 時(shí)間: 2025-3-22 06:03 作者: 籠子 時(shí)間: 2025-3-22 10:42 作者: Antagonism 時(shí)間: 2025-3-22 16:47
978-3-031-01034-7Springer Nature Switzerland AG 2016作者: 入伍儀式 時(shí)間: 2025-3-22 17:13 作者: 內(nèi)行 時(shí)間: 2025-3-23 00:45 作者: 未完成 時(shí)間: 2025-3-23 02:43 作者: cumulative 時(shí)間: 2025-3-23 07:54 作者: Flavouring 時(shí)間: 2025-3-23 12:53
Advanced Disambiguation Methods,ploying LLKBs. First, we consider the machine learning paradigm of distant supervision to generate training data, and second, we discuss recent work in Deep Learning on continuous vector space models of KBs and LKBs. We start by introducing the task of . (AKBC), because it is one of the core tasks c作者: 加入 時(shí)間: 2025-3-23 16:12 作者: ARCH 時(shí)間: 2025-3-23 20:54 作者: 流出 時(shí)間: 2025-3-24 01:03 作者: 水獺 時(shí)間: 2025-3-24 04:23 作者: Consensus 時(shí)間: 2025-3-24 06:31
1947-4040 This allows us to not only extend the range of covered words and senses, but also gives us the opportunity to obtain a richer knowledge representation when a p978-3-031-01034-7978-3-031-02162-6Series ISSN 1947-4040 Series E-ISSN 1947-4059 作者: 新奇 時(shí)間: 2025-3-24 13:00
Iryna Gurevych,Judith Eckle-Kohler,Michael Matuschek作者: 土坯 時(shí)間: 2025-3-24 18:27
Iryna Gurevych,Judith Eckle-Kohler,Michael Matuschek作者: 合并 時(shí)間: 2025-3-24 20:42 作者: Expand 時(shí)間: 2025-3-24 23:43
Iryna Gurevych,Judith Eckle-Kohler,Michael Matuschek作者: 冷淡周邊 時(shí)間: 2025-3-25 03:28
Iryna Gurevych,Judith Eckle-Kohler,Michael Matuschek作者: 機(jī)密 時(shí)間: 2025-3-25 10:47
Iryna Gurevych,Judith Eckle-Kohler,Michael Matuschek作者: AGGER 時(shí)間: 2025-3-25 13:48
Iryna Gurevych,Judith Eckle-Kohler,Michael Matuschek作者: 憎惡 時(shí)間: 2025-3-25 17:20
Iryna Gurevych,Judith Eckle-Kohler,Michael Matuschek作者: Chagrin 時(shí)間: 2025-3-25 21:26
Lexical Knowledge Bases,t we define our terminology, then we give a broad overview of various kinds of LKBs that play an important role in NLP. For particular resource-specific details, we refer the reader to the respective reference publications.作者: Somber 時(shí)間: 2025-3-26 03:43 作者: GROWL 時(shí)間: 2025-3-26 04:31 作者: Blood-Clot 時(shí)間: 2025-3-26 09:19
Advanced Disambiguation Methods,n Deep Learning on continuous vector space models of KBs and LKBs. We start by introducing the task of . (AKBC), because it is one of the core tasks considered both within distant supervision and vector space modeling of KBs.作者: Gene408 時(shí)間: 2025-3-26 15:27
1947-4040 tives, focusing on their construction and use in natural language processing (NLP). It characterizes a wide range of both expert-based and collaboratively constructed lexical knowledge bases. Only basic familiarity with NLP is required and this book has been written for both students and researchers作者: 中國(guó)紀(jì)念碑 時(shí)間: 2025-3-26 17:24
Book 2016using on their construction and use in natural language processing (NLP). It characterizes a wide range of both expert-based and collaboratively constructed lexical knowledge bases. Only basic familiarity with NLP is required and this book has been written for both students and researchers in NLP an作者: HUMP 時(shí)間: 2025-3-27 00:14
8樓作者: 善變 時(shí)間: 2025-3-27 04:15
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9樓作者: 職業(yè)拳擊手 時(shí)間: 2025-3-28 01:55
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