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            2. 學會通知



              中國中文信息學會會員發展工作的通知

               


                      為推進學會的改革,建立以會員為主體的管理體制,健全會員管理制度,按照中國科協《關于規范全國性學會個人會員登記號的通知》的要求和規定,結合本會的具體情況,建立個人會員登記制度。

              會員登記的簡要流程:

              1.請有意申請者下載并填寫"

              2.將填寫完整的"會員信息登記表"通過電子郵件方式發送至學會辦公室會員部(huangyi@iscas.ac.cn

              3.請任選以下三種方式之一繳納會費:

                  1)銀行轉賬:

                      開戶銀行:工商行北京市分行海淀西區支行

                      戶        名:中國中文信息學會

                      賬        號:0200004509014415619

                      注:請在附言中注明會員姓名

                  2)郵局匯款:

                      地        址:北京8718信箱"中國中文信息學會"

                      收   款 人:中國中文信息學會

                      郵政編碼:100190

                      聯系電話:010-62562916

                      注:請在附言中注明會員姓名

                  3)中國中文信息學會辦公室繳納

                      地        址:北京海淀區中關村南四街4號院7號樓201房間

                      聯系電話:010-62562916


              2013年度"中國中文信息學會"個人會員收費標準:

                      個人會員:120元/年

                      學生會員:  60元/年


                      會員經注冊并繳費后,將獲得會員登記號和會員證。在參加學會主辦的各類學術活動時,憑會員證將享受會費優惠;定期獲贈中國中文信息學會會員通訊(電子版)。

                      為鼓勵更多學者加入學會,完成2013度會員登記的全體會員和部分學生會員(以繳費順序,先到先得,贈完為止),將獲贈2013年度全年《中文信息學報》(紙質版)。


              另附中國中文信息學會章程


              中國中文信息學會
              2013年3月15日

              學術活動



              IJCAI Workshop - August 3-5,2013,Bejing,China

               


              About

              This workshop will explore the novel use of techniques from machine learning, data mining, text mining, information retrieval, statistics, information security and privacy, and user modelling, to identify patterns of potentially positive and negative activities in social media by examining the online content, social interactions, and user behaviours. It will also study the metrics in measuring the positive and negative impact of social media on individuals, business organizations, and government agencies. The analysis and mining of these patterns aim to promote positive activities in social medial, while at the same time reveal harmful aspects of social media and suggest ways to tackle and to overcome the negative side.


              Objectives

              In recent years, social media has continued to grow in popularity and has become a powerful platform for people to unite together under common interests. The explosive use of social media has turned it into a double-edged sword. On the one hand, the information revolution has proven to have a positive impact in society. Social platforms introduce a canvas for self-expression where users can create, manipulate and share content. Positive impacts of these platforms in society include their use in bringing information out of conflicted nations to the World. They have also proven to be an effective way of propagating information, proving to spread the word before mainstream media prints a story. This has been particularly useful for word spreading-based mobilisation in emergency response and crisis situations.


              On the other hand, social media platforms have appeared to be also the catalyst in fuelling violent events. The proliferation of insults and personal attacks online along with the appearance of socially disruptive patterns in online social behaviour has become more and more common. Young people are becoming increasingly narcissistic, and obsessed with self-image and shallow friendships partly due to the use of Facebook and other social media platforms. Social media addiction also leads to low self-esteem and even anti-social behaviours.


              The aim of this workshop is to bring together researchers from various backgrounds including those from computer science, social science, and psychology, to discuss the current and emergent topics, and cutting-edge approaches to address issues relating to both positive and negative sides of social media.


              Important Dates

              • April 20, 2013 – Paper submission deadline
              • May 20, 2013 – Paper acceptance notification
              • May 30, 2013 – Camera-ready copy due

              Contact

              E-mail: pansom13@easychair.org

              Twitter hashtag: #pansom13


              詳細內容:

              http://t.cn/zY3hYgz

              百度校園電影推薦系統算法創新大賽正式啟動

               


                      百度舉辦"電影推薦算法創新大賽",旨在挖掘更多高精尖的技術開發人員,該活動已于2013年3月1日正式上線。無論你是在讀的本科生、還是正在從事數據挖掘研究的從業者,碩士、博士生抑或是在異國讀書的學生,只要你有過硬的技術才能、有十足的工作熱情,都可以報名參與到活動中來。


                      網絡內容如此豐富多彩,信息量大到讓我們難以駕馭,內容推薦引擎在這個時候就派上了用場,它可以根據我們的喜好,甚至分析我們的用戶行為為我們推薦我們想要的內容。在國外,這些年來細分領域的推薦引擎如雨后春筍紛紛拔地而起,比如,音樂推薦有Pandora,書的推薦有 GoodRead,視頻的推薦有Netflix等等。如此多的推薦引擎又讓用戶目不暇接,用戶會定期收到各種內容推薦。反觀國內,互聯網的飛速發展,無線城市的進程不斷加快,無處不在的WiFi熱點,使筆記本電腦、平板、手機逐漸成為人們娛樂休閑的主要手段,人們觀看視頻的習慣逐漸從電視轉移到了網絡,但是由于互聯網上的視頻數據量呈幾何級數增長,人們的選擇越來越豐富,想看點電影、視頻,反倒不知道如何選擇。


                      百度作為全球最大的中文搜索引擎,一直致力于不斷地擴展搜索范圍和深度,為用戶帶來最舒適的搜索體驗是百度始終堅持的目標。針對用戶無法有效找到感興趣的影片這個問題,百度已經成立專門的技術團隊去用戶授權的社交網絡抓取視頻,根據用戶的社交關系和瀏覽歷史分析用戶的興趣、進行視頻推薦,并以最優的順序展現給用戶??v觀國內互聯網市場,使用網絡視頻的用戶數已達1.7個億,面對如此龐大的用戶群進行信息的提取、分析、計算,團隊的工作量可謂十分巨大,為了擴大技術開發團隊的規模,百度特此舉辦"電影推薦算法創新大賽",希望通過這個活動挖掘更多高精尖的技術開發人員,該活動已于2013年3月1日正式上線。無論你是在讀的本科生、還是正在從事數據挖掘研究的從業者,碩士、博士生抑或是在異國讀書的學生,只要你有過硬的技術才能、有十足的工作熱情,都可以報名參與到活動中來。


                      本次校園品牌部活動的技術和數據支持均來自百度垂直搜索部門的技術團隊,領隊由普林斯頓榮譽歸來的汪冠春博士以及賓夕法尼亞大學的胡一川擔任,團隊的其他幾名成員也均來自百度的技術班底。為了增加活動的參與性,主辦方還設置了豐厚的活動獎金,一等獎可達10000元,在此你將有機會與世界頂尖高校的博士生,也是本次活動的主辦人汪冠春,胡一川等技術大牛過招,交流技術,探討算法。除此之外,在活動中脫穎而出者還將有進入百度的技術部門實習的機會,進而成為百度的一員!快來參加"電影推薦算法創新大賽"的活動吧!活動網址是:http://openresearch.baidu.com/topic/40.jspx,你將有機會加入到一個高智商的技術團隊,實現你的技術夢想并獲得意想不到的豐厚大獎,還在等什么呢?!


              Google Research Releases Wikilinks Corpus With 40M Mentions And 3M Entities

               


              Google Research just launched its Wikilinks corpus, a massive new data set for developers and researchers that could make it easier to add smart disambiguation and cross-referencing to their applications. The data could, for example, make it easier to find out if two web sites are talking about the same person or concept, Google says. In total, the corpus features 40 million disambiguated mentions found within 10 million web pages. This, Google notes, makes it "over 100 times bigger than the next largest corpus," which features fewer than 100,000 mentions.


              For Google, of course, disambiguation is something that is a core feature of the Knowledge Graph project, which allows you to tell Google whether you are looking for links related to the planet, car or chemical element when you search for '

              To construct this data set, Google looked at links to Wikipedia pages "where the anchor text of the link closely matches the title of the target Wikipedia page." There is a high probability that this anchor text is a mention of the corresponding entity that's the focus of the entity that's discussed in the Wikipedia entry.


              The 10 million annotated web pages, sadly, aren't part of the corpus because of copyright issues, but the UMass Wikilinks project features all the necessary tools to create this data from scratch. The UMass team also published a paper that explains the process that was used to create this data set in more detail (PDF).


              Last year, Google released a similar data set when it launched a database with over 7.5 million concepts and 175 million unique text strings, which is similar to what Google itself uses to suggest targeted keywords for advertisers. That set, too, was built by looking at Wikipedia articles to identify concepts and the anchor links that other websites used to link to them.


              詳細內容:

              http://t.cn/zYmoqft

              一個命名排歧(name disambiguation)的數據集

              Xuezhi Wang, Jie Tang, Hong Cheng, Philip S. Yu

              KEG Group, Tsinghua University, China

               


              Introduction

              Name ambiguity has long been viewed as a challenging problem in many applications, such as scientific literature management, people search, and social network analysis. When we search a person name in these systems, many documents (e.g., papers, webpages) containing that person's name may be returned. Which documents are about the person we care about? Although much research has been conducted, the problem remains largely unsolved, especially with the rapid growth of the people information available on the Web.


              We share related data sets and our ideas for name disambiguation on this page. If you use the data for publication, please kindly cite the following papers:

              @article{Tang:12TKDE,

                  author = {Jie Tang and Alvis C.M. Fong and Bo Wang and Jing Zhang},

                  title = {A Unified Probabilistic Framework for Name Disambiguation in Digital Library},

                  journal ={IEEE Transactions on Knowledge and Data Engineering},

                  volume = {24},

                  mber = {6},

                  year = {2012},

              }


              @INPROCEEDINGS{ wang:adana:,

                  AUTHOR = "Xuezhi Wang and Jie Tang and Hong Cheng and Philip S. Yu",

                  TITLE = "ADANA: Active Name Disambiguation",

                  BOOKTITLE = "ICDM'11",

                  PAGES = {794-803},

                  YEAR = {2011},

              }

              [PDF] [Slides] [Simple Version Download , Readme] [Simple Version Download , Readme]

              詳細內容:

              http://t.cn/zYnThJz

               


              數據挖掘、數據分析、人工智能及機器學習課程資源



              Easyhadoop技術大學:大數據實戰專業培訓班


              詳細內容:http://t.cn/zY8JjWq


              WWW 2013 Accepted Papers


              詳細內容:http://www2013.org/2013/02/18/www2013-accepted-papers/


              Stanford Deep Learning Wiki


              詳細內容:http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial


              《程序員》雜志2013年2月刊發表的科普文章: 深度學習 - 機器學習的新浪潮


              詳細內容:文章:http://t.cn/zY3iQdZ


              《中文信息學報》目錄



              《中文信息學報》第27卷,第1期 2013年1月目錄

               


              題目

              作者

              頁碼

              基于大規模語料庫的漢語詞義相似度計算方法 石 靜, 吳云芳, 邱立坤, 呂學強

              1

              一種基于搭配的中文詞匯語義相似度計算方法 王 石,曹存根,裴亞軍,夏 飛

              7

              基于雙語依存關系映射的中英文詞表構建研究 徐 華,劉丹丹,錢龍華,周國棟

              15

              網頁中商品"屬性—值"關系的自動抽取方法研究 唐 偉,洪 宇,馮艷卉,姚建民,朱巧明

              21

              事件超圖模型及類型識別 肖 升,何炎祥

              30

              一種基于社會化標簽的信息檢索方法 李 鵬,王 斌,晉 薇

              39

              中文博客多方面話題情感分析研究 傅向華, 劉 國, 郭巖巖, 郭武彪

              47

              第三屆中文傾向性分析評測(COAE2011)語料的構建與分析 廖祥文,許洪波,孫 樂,姚天昉

              56

              統計機器翻譯中一致性解碼方法比較分析 段 楠,李 沐,周 明

              64

              BFSCTC漢語句義結構標注語料庫 劉盈盈,羅森林,馮 揚,韓 磊,陳 功,王 倩

              72

              基于統計的記敘文語句焦點的分布特點研究 趙建軍, 楊玉芳, 呂士楠

              81

              基于組合核的蛋白質交互關系抽取 李麗雙,劉 洋,黃德根

              86

              "方言同音字匯"自動生成軟件的設計及實現 程南昌, 侯 敏

              93

              針對發音質量評測的聲學模型優化算法 嚴 可,魏 思,戴禮榮

              98

              新標準體系下蒙古文變形顯現模型的設計與實現 王 震,劉匯丹,吳 健

              108

              現代藏語助動詞結尾句子邊界識別方法 趙維納, 于 新,劉匯丹,李 琳,王 磊,吳 健

              115

              水書鍵盤輸入系統研究與實現 陳笑蓉,楊撼岳,鄭高山,黃 千

              120


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