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ھŽйϢѧԼѧУɹٰ

    201481517գھŽйϢѧԼѧУѧѧԺɹٰ졣йϢѧἰ΢оԺԼѧУ2005ѳɹٰ˽졣ѧУڳа췽ҵѧԼоĵĽ߳Ŭ£˹ʦձΪȻԼ˲ͼƹ׿ԽףǧƵѧѧУлԹⶥҵоѧߵĵָdzԽйϢѧһѧУƹ㵽ȫ֪У

    2014ѧУѧѧԺϢоа졣ѧУ뵽΢ٶȡѧпԺ6λרΪѧԱڿΡУ΢оԺϯоԱʿоԺΤ粩ʿýݷоչпԺϢооԱΪdzĽϢĹؼѧѧоڽƪ½չٶȻԴ⻪ʿ˻뼼ۺӦãٶѧϰоԺԺǧ˼ƻר࿭ʿΪ˻ѧϰķչѧϰݡ

    йϢѧ³ҵѧڣѧУʼ΢оԺڣѧѧԺԺǧ˼ƻרҵڣϯѧڣǧ˼ƻרδΪڳϯѧУ´ǡ51УоҵĽ200оʦоԱһãȡΪʱĿγ̣ѧԱģﵽͨȻԴؼ½չѧϰ֣̽ҲѧãҲȻԴķչǰģ׷ױʾϣѧУܹԽԽãΪȻԴҪƽ̨ΪȻԴؼķչ

ѧ๤ίִίԱоԱԾоԱѧĻCOLING2014Ľ

    829գڵ25ѧʻCOLING2014佱ʽϣѧ깤ίԱִίԱоԱѧԾоԱѧ"Relation Classification via Convolutional Deep Neural Network"ߣ˹ΩܹУԾôġIBM Watson Best Paper Award

    COLINGǼѧĶʻ飬ɹʼѧѧᣨthe International Committee on Computational LinguisticsICCL)죬ÿһ졣201482329ڰĶٿؽ700˲μ˻顣λ鹲յ691ƪģ¼ÿͷ139ƪ¼Ϊ20.1%ȫ缸ʮλȨרɵίԱͨͶƱƪģĵƱеһõ˹ͬеĹ㷺עΪǸһԵĹԹϵһҪӰ졣COLING2014ĽIBM WatsonоĹ2011Ƴһ̨ܿٻشȻԸĻWatsonʴĿΣձԵսʤѡ֡

    ϴڴķǽṹıš͡ʼͨšļ¼ȡΰЩݣձĹ۵ͨעϢѷǽṹıɽṹıеĹؼ֮һʵϵࡣͳĹϵҪмලķоصҳԵͳȡƾ辭УеȻԴߣԱע䷨ʵʶȣȻڴģϢĿǰȻԴޣͬʱͳҲᵼ´еۻЩ⣬û˻ھ磨Convolutional Deep Neural Networkıѧϰþ磬ԶѧϰʵϵĴʻԼʵڵľıȣϵ෽÷ҪNLPߣPOSNERParsingȣȡĸȡжۻ⡣ʵstate-of-the-art,÷ڹϵ

 

ѧ

ڰ˽ȫϢֻᣨCIPT2014

    ΪֽϢе³ɹƽϢķչйϢѧẺϢϵͳרҵίԱᡢϢרҵίԱᡢֱϢרҵίԱᡢѯίԱὫ201410µڱٿȫϢֻᣨThe 8th China National Conference on Chinese Character Information Processing Techniques,CIPT2014ɺϢϵͳרίа졣ֿͻӭϢصרѧ߲λᡣ

    Ҫ

    Ͷֹڣ 2014915

    ¼֪ͨʱ䣺2014930ǰ

    ڣ20141027-28

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ʮȫѧѧ齫20141018ա19人

     "ʮȫѧѧ"The Thirteenth China National Conferenceon Computational Linguistics,CCL201420141018ա19ڻʦѧСΪȻԴרѧߵ֯йϢѧᣨCIPS콢飬ȫѧ1991꿪ʼÿٰһΣ2013꿪ʼÿٰһΡCCLйڸԵļ㴦Ϊѧµѧͼɹṩ˹㷺Ľƽ̨

    Ҫ

    ʱ䣺201410178:00

    ٿʱ䣺2014101819գ죩

    ϸοվ

ڶȫ֪ʶͼֻὫ20141017人

     ֪ʶͼףKnowledge Graphǵǰѧҵоȵ㡣֪ʶͼ׵ĹϢϢҪļֵΪȻԴרѧߵѧ塪йϢѧᣨCIPSǰѧֻ֮һλ齫ʮȫѧͬһصٰ죬񡢽ͨϸϢ鿴ʮȫѧվеIJλ˵֡

    Ҫ

    ʱ䣺2014101614:00

    ٿʱ䣺20141017գһ죩

CIPS-SIGHANĴԴʻ飨CLP-2014)20141020ա21人

     2014ĴԴʻ飨CLP-2014йϢѧᣨCIPS͹ʼѧЭĴרҵȤ飨SIGHAN֯׽ĴԴʻ飨CLP-201023ʼѧᣨCOLING-2010ͬڱٰ졣ڶĴԴʻ飨CLP201220121220-21йѧСCLP201420141020-21йʦѧС

     ĴԴʻ飨CLP-2014ּΪĴȫоԱṩһչʾоɹѧ˼롢̽о·ƶоչƽ̨CLP-2014ٰһ⾺ķִʡƴд顢ľ䷨Գȡйش˴ι⾺ϸϢμ clp2014/webpage/cn/bake-off.htmλ齫ʮȫѧͬһصٰ죬񡢽ͨϸϢ鿴ʮȫѧվеIJλ˵֡

    Ҫ

    ʱ䣺201410198:00

    ٿʱ䣺2014102021գ죩

ѧ֪ͨ

йϢѧԱչ֪ͨ

    ΪƽѧĸĸԻԱΪĹƣȫԱƶȣйЭڹ淶ȫѧ˻ԱǼǺŵ֪ͨҪ͹涨ϱľ˻ԱǼƶȡ

ԱǼǵļҪ:

    1.Աдɺѧ䣺cips_m@iscas.ac.cn

    2.յԱϢȷϺѧȻ, ɻԱʸ֤

2014"йϢѧ"˻Աշѱ׼

    ˻Ա120Ԫ/    ѧԱ 60Ԫ/

Աѽɷѷʽ

    1 תˣ

       Убзк֧ йϢѧ ˺ţ0200004509014415619

    2 ʾֻ

       ַ8718"йϢѧ" տˣйϢѧ 100190

    3 ѧ֧˺תˣ

       йϢѧ ˺ţcips_pay@163.com

    4йϢѧ칫ҽɷ

       ַкйشĽ4Ժ7¥201 ϵ绰010-62562916

    ԱעᲢɷѺ󣬽ûԱǼǺźͻԱ֤ڲμѧĸѧʱƾԱ֤ܻŻݣڻйϢѧԱͨѶӰ棩

    Ϊѧ߼ѧᣬ2014ȻԱǼǵȫԱͲѧԱԽɷ˳ȵȵãΪֹ2014ȫ꡶Ϣѧֽʰ棩

ġѧ̬

SIGIR 2015 89-13ʥǸ

    SIGIR 2015 89-13ʥǸСͶֹΪ2015128ա

    http://www.sigir2015.org/

COLING 2016ձٰ

    ڰֵCOLING 2014ĻʽϣίԱCOLING 2016ձٰ죬ձ鱨ͨоNICT졣


Twitter˺ſķ

    Twitter ǰ˹ʦ Ian Chan ǰTwitter ûķߣTweet Activity Analytics Twitter 7 Ƴһ Google Analytics ƵıߣʹøùߵûԶԼĽָһʱڵعȡȡ֮ǰܽԹţԷǸõĺԼ Twitter ͶŹЧӽܽ˺ſʹ 14 죬漰ޡܱԼͣõ˺šңɷҪΪӢ


IBM߱Ķѧϰ˹WatsonͶʹãʼڿԱ

    IBM828µ Watson ϵͳͶʹãʼΪһЩѧҷ񣬼оĽչIBM µ˹ܿĶס⻯ѧӦʽͼ

    Watson һ̨ 90 ̨ IBM Power 7 ɡ Google΢˹ȣӦӲоƬܾͿʼģԪ IBM "DeepQA" ȡ⣬ں̺ѰҴ𰸲ȻԻش

    http://www.36kr.com/p/214911.html

ƴѶܼͥƷ

    գƴѶ(002230,ɰ)ھ"ƴѶܼͥƷ""Ѷɳ"ƻ

    ˽⣬Ʒܼͥ򣬰°汾Ϭ3.0Ѷ䡢ܵӽ"δң"ȡƴѶɶ³ϸ˽ƴѶ˹ܷĴ·չƴѶɻͶϴҵͶʻĿͶʡ

    http://tech.hexun.com/2014-09-01/168086162.html

塢ѧԴ

CIKM 2014 Accepted Papers
http://cikm2014.fudan.edu.cn/index.php/Index/info/id/11

Deep Learning KDD 2014 Tutorial
http://www.cs.toronto.edu/~rsalakhu/kdd.html
    Russ SalakhutdinovKDD 2014иĹDeep Learning tutorialRBMs, DBMs, DBNs, multimodal learningӺʹhttp://deeplearning.cs.toronto.edu/

Tutorial: Statistical Methods for Mining Big Text Data
http://www.itee.uq.edu.au/dke/filething/get/855/text-mining-ChengXiangZhai.pdf
    Գʦ(UIUC)ڰĴݿⲩʿѵĽ̳:"Statistical Methods for Mining Big Text Data" ֻͳģ(Statistics Language Model)Ļģ(Topic Model): LDAPLSAԭӦáгδо

΢оԺڷѧϰۻԴ
http://research.microsoft.com/en-us/events/fs2013/agenda_collapsed.aspx
    Li Deng, John Platt ΢Yoshua BengioѧHonglak LeeЪ, Andrew Ng ˹̹, Ruslan Salakhutdinov׶ࣩ˵ıPPTƵ

˹̹ģݼȫ
https://snap.stanford.edu/data/
    ˹̹Jure Leskovecءʮֲͬ͵ݼ罻;;ʼ;;WebȵȣFriendsterݼ6ǧ5ڵ㣬18ߡ

ŦԼʱעݼ
    https://code.google.com/p/nyt-salience/
    ѵ100,834ļ19,261,118עʵ塣Լϰ9,706ļ187,080עʵ塣

רGraph-Based Semi-Supervised Learning
    http://www.morganclaypool.com/doi/abs/10.2200/S00590ED1V01Y201408AIM029
    While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.


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