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Titlebook: Natural Language Processing and Chinese Computing; First CCF Conference Ming Zhou,Guodong Zhou,Lei Zou Conference proceedings 2012 Springer

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書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing
副標(biāo)題First CCF Conference
編輯Ming Zhou,Guodong Zhou,Lei Zou
視頻videohttp://file.papertrans.cn/662/661813/661813.mp4
概述Fast-track conference proceedings.State-of-the-art research.Up-to-date results
叢書(shū)名稱(chēng)Communications in Computer and Information Science
圖書(shū)封面Titlebook: Natural Language Processing and Chinese Computing; First CCF Conference Ming Zhou,Guodong Zhou,Lei Zou Conference proceedings 2012 Springer
描述This book constitutes the refereed proceedings of the First CCF Conference, NLPCC 2012, held in Beijing, China, during October/November, 2012. The 43 revised full papers presented were carefully reviewed and selected from 151 submissions. The papers are organized in topical sections on applications on language computing; fundamentals on language computing; machine translation and multi-lingual information access; NLP for search, ads and social networks; question answering and Web mining.
出版日期Conference proceedings 2012
關(guān)鍵詞Chinese computing; applications of natural language processing; language translation and linguistics; m
版次1
doihttps://doi.org/10.1007/978-3-642-34456-5
isbn_softcover978-3-642-34455-8
isbn_ebook978-3-642-34456-5Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer-Verlag Berlin Heidelberg 2012
The information of publication is updating

書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing影響因子(影響力)




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing被引頻次




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing被引頻次學(xué)科排名




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing年度引用




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing年度引用學(xué)科排名




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing讀者反饋




書(shū)目名稱(chēng)Natural Language Processing and Chinese Computing讀者反饋學(xué)科排名




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Automatic Generation of Chinese Character Based on Human Vision and Prior Knowledge of Calligraphyive layer framework. Calligraphist’s unique calligraphy skill is analyzed and his particular strokes, radicals and layout patterns provide raw element for the proposed five layers. The criteria of visual aesthetics based on Marr’s vision assumption are built for the proposed algorithm of automatic g
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A Spoken Dialogue System Based on FST and DBNrmance of the whole system. This paper proposes a novel method named action-group finite state transducer (FST) model to cope with the problem of natural language understanding. This model can map user utterances to actions, and extract user’s information according to the matched string. For dialogu
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Ontology-Based Event Modeling for Semantic Understanding of Chinese News Storyer, most existing work deals with documents so they fail to provide sufficient semantic information about events in news articles. In this paper, considering ., .hy and ., the 5W1H elements of a piece of news, we propose a News Ontology Event Model (NOEM) which can describe 5W1H semantic elements of
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Topic Structure Identification of PClause Sequence Based on Generalized Topic Theorycteristics of topic structure and the stack model having been clearly explained by Generalized Topic Theory, proposes a scheme for identifying the topic structure of PClause sequence. The accuracy rate for open test is 15 percent higher than the baseline, which proves the effectiveness of employing
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Chinese Semantic Role Labeling with Dependency-Driven Constituent Parse Tree Structure (D-CPT) structure is proposed to better represent the dependency relations in a CPT-style structure, which employs dependency relation types instead of phrase labels in CPT. In this way, D-CPT not only keeps the dependency relationship information in the dependency parse tree (DPT) structure but al
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