Abstract:

The 12th China National Conference on Computational Linguistics (CCL) will take place in Suzhou(http://en.wikipedia.org/wiki/Suzhou), China, 10-12 October 2013, hosted by Soochow University(http://www.suda.edu.cn). CCL, a bi-annual conference starting from 1991 and the flagship conference of the Chinese Information Processing Society (CIPS), the largest NLP scholar and expert community in China, is a premier nation-wide forum for disseminating new scholarly and technological work in computational linguistics, with major emphasis on computer processing of the languages in China, including Chinese and minority languages such as Tibetan, Mongolian and Uyghur.

Papers submitted to CCL can be in Chinese or English. Accepted papers in Chinese will be published in the Journal of Chinese Information Processing, the most influential Journal in Computational Linguistics in China, and accepted papers in English will be published by Springer in the Lecture Notes in Artificial Intelligence (LNAI) series.

Attached to the 12th CCL, we start up the First International Symposium on Natural Language Processing based on Naturally Annotated Big Data (1st NLP-NABD). NLP-NABD covers all the NLP topics, as listed above, with particular interest in the cutting edge methodologies and technologies of natural language processing in the era of big data. The so-called “naturally annotated” means different type of annotations on varieties of Web resources which are “unconsciously handcrafted” by Web users for their own purposes (in general, nothing to do with natural language processing), but can be used by computational linguists in a conscious and systematic way for various tasks of natural language processing, for examples, punctuation marks in Chinese can benefit word boundaries identification, social tags in social media can benefit keyword extraction, “categories” given in Wikipedia can benefit text categorization. The natural annotation can be explicit, as in above examples, or can be implicit, as “NOUN and other NPs” in “Beijing and other cities” as well as “NPs such as NOUN” in “cities such as Beijing”. This symposium aims at numerous research challenges ranging from very-large-scale unsupervised/semi-supervised machine leaning(deep learning for instance) of naturally annotated big data to integration of the learned resources and models with existing handcrafted “core” resources and “core” language computing models.

NLP-NABD targets at computation of any languages in the world. Only papers in English are accepted and will also be included in LNAI, together with the accepted English papers of 12th CCL.

Conference Topics：

• Cognitive modelling of language processing and psycholinguistics
• Discourse, coreference and pragmatics
• Evaluation methoda
• Language resources and annotation
• Lexical semantics and ontologies
• Large-scale knowledge acquisition and reasoning
• Machine translation
• Multilinguality in NLP
• NLP applications
• NLP for social media
• Named entity recognition and linking
• Open-domain question answering
• Sentiment analysis, opinion mining and text classification
• Social computing
• Speech recognition and synthesis
• Statistical and machine learning methods in NLP
• Summarization and generation
• Syntactic analysis and parsing the Web
• Tagging and chunking
• Textual entailment
• Text mining, open-domain information extraction and machine reading of the Web
• Web information retrieval
• Underresourced languages processing
• Word segmentation
• Word sense disambiguation

Papers submitted to CCL can be in Chinese or English. Accepted papers in Chinese will be published in the Journal of Chinese Information Processing, the most influential Journal in Computational Linguistics in China, and accepted papers in English will be published by Springer in the Lecture Notes in Artificial Intelligence (LNAI) series.

NLP-NABD targets at computation of any languages in the world. Only papers in English are accepted and will also be included in LNAI, together with the accepted English papers of 12th CCL.

Downloads:

Important dates

• Paper submission deadline: 1 June 2013  12 June 2013
• Notification of acceptance: 30 June 2013  5 July 2013
• Camera ready submission: 15 July 2013

Deadline：

• Paper submission deadline: 1 June 2013  12 June 2013
• Notification of acceptance: 30 June 2013  5 July 2013
• Camera ready submission: 15 July 2013

Submission:

Papers in English submitted to 12th CCL and 1st NLP-NABD should not exceed 12 pages following Springer's manuscript submission guideline:
http://www.springer.de/comp/lncs/authors.html.
Papers in Chinese submitted to 12th CCL should follow the submission guideline given at the style file below. Please note that there is page limitation, but the total number of words is strictly limited to 8,000 Chinese characters, including references.
CNCCL-2013-Style-File.doc
LNAI will submit the published papers to EI Compendex for indexing automatically, and the chance of being indexed is high according to previous experience of Springer. More information about the indexing of LNAI, please refer to the link:
http://www.springer.com/computer/lncs?SGWID=0-164-6-1068921-0.

Papers will be double-blindly reviewed without the authors' names and affiliations included. Furthermore, self-references that reveal the author's identity, e.g., "We previously showed (Smith, 1991) ...", must be avoided. Instead, use citations such as "Smith (1991) previously showed ...". Papers that do not conform to these requirements will be rejected without review. Submission will be electronic in PDF format through the START system at:
https://www.softconf.com/e/cnccl2013/

General Chairs of 12th CCL

Program Committee：

Organizing Committee：

2013 Annual Meeting of China Association of
Chinese Information (CIPS 2013) and

The 12th National Conference on
Computational Linguistics (CCL 2013) Formal notice

        After the sixth director office meeting of the Seventh Council of China Chinese Information Society, Chinese Information Society Annual Meeting and Council (CIPS 2013) will be held in Soochow University in October 9, 2013.Chinese Information Processing (including Chinese and minority languages ​​Information Processing) plays an important role in in the development of science technology and industry in the field of information technology development.The development of computer and Internet technology brings new challenge to Chinese information processing technology.Chinese information processing has become a new strategy of long-term development of information in science and technology.The academic years will be a majority of Chinese experts and scholars in the field of information processing (about 300 people) an event which will effectively promote the Chinese information processing field of theoretical innovation, research and technological exchanges and cooperation!We invite everyone to participate in Chinese Information Processing Society 2013 Annual Meeting to share, to discuss efforts to promote the field of Chinese information processing technology and industry innovation and development！The meeting's theme is：Depth learning and Chinese Information Processing.We will invite leading experts to do special reports, and conduct seminars related topics.

        The Twelfth China National Conference on Computational Linguistics, CCL 2013 will be held October 10, 2013 -12 days held at the University in Suzhou.As the largest natural language processing experts community organizations - Chinese Information Processing Society (CIPS) is the flagship conference, the National Conference of Computational Linguistics from 1991 held biennially.CCL focuses on Chinese territory of various languages ​​(such as Chinese, Tibetan, Mongolian and Uygur, etc.) calculation processing, computational linguistics for the dissemination of the latest academic and technical achievement provides a broad platform for the exchange.Meanwhile, "The First Big Data based on Natural Marked International Symposium on Natural Language Processing" (NLP-NABD 2013) will be held jointly with the CCL 2013.NLP-NABD pays special attention to big data era frontier of natural language processing methods and techniques and will focus on domestic and foreign large data based on the natural direction marked on a variety of cutting-edge research,including: How big natural label is valid for large-scale data unsupervised / semi-supervised machine learning (eg deep learning), how to learn to resources, models and existing manually labeled core resources and computational model combining the core language .

       During the conference, Chinese Information Processing Society will hold the 1st Conference on Chinese Knowledge Graph.Knowledge Graph is the current academic and business research hotspot.Chinese Knowledge Map Construction for Chinese information processing and Chinese information retrieval has important value.The seminar will focus on mapping knowledge map construction of Chinese knowledge resources, technology, programs, policies, and issues and challenges to be studied, and promote research units as well as between the research community and industry, academic exchanges between, to explore the future large-scale Chinese knowledge map construction discussion and cooperation mechanisms.So far, the Assembly has been domestic natural language processing researchers' attention, currently has about 10 engaged in research and practice knowledge map famous universities, research institutions and business experts and academics who wish to participate and make a speech.

        The meeting notice and meeting receipt send, please follow the notice on time to the designated locations and attend the meeting.

Invited Speakers

Randy Goebel

University of Alberta, Canada

Topic:Natural Language Processing: The Challenge of Compressing Data to Models.

Summary:The problem of machine processing of natural language (NLP) has long been a research focus of artificial intelligence. This is partly because the use of natural language is easily conceived as a cognitive task requiring human-like intelligence. It is also because the rational structures for computer interpretation of language require the full suite of computational tools developed over the last hundred years (grammar, dictionaries, logic, parsing, inference, and context management). Most of the recent practical advances in NLP have arisen in the context of simple machine learning applied to large language corpora, to induce fragments of language models that provide the basis for interpretive and generative manipulation of language. These largely statistical models are arisen in what has been called the "pendulum swing" of NLP, in which statistical models have recently dominated those based on structural linguistics. In this lecture, we look at the concept of noisy corpora and their role in language models, including some interesting alternative sources of data for building language models. The applications range from complex language summary to the information extraction from medical, legal, and historical documents.

Resume:Randy Goebel is a professor in the department of Computing Science at the University of Alberta, in Edmonton, Alberta, Canada.

        He is also vice president of the innovates Centre of Research Excellence (iCORE) at Alberta Innovates Technology Futures (AITF: www.albertainnovates.ca), chair of the Alberta Innovates Academy, and principle investigator in the Alberta Innovates Centre for Machine Learning (www.aicml.ca). He received the B.Sc. (Computer Science), M.Sc. (Computing Science), and Ph.D. (Computer Science) from the Universities of Regina, Alberta, and British Columbia, respectively.

        At AITF, Randy is in charge of reshaping research investments (graduate student scholarships, research chairs, research centres). His research interests include applications of machine learning to systems biology, visualization, and web mining, as well as work on natural language processing, web semantics, and belief revision. Randy has experience working on industrial research projects in crew scheduling, pipeline scheduling, and steel mill scheduling, as well as scheduling and optimization projects for the energy industry in Alberta.

        Randy has held appointments at the University of Waterloo, University of Tokyo, Multimedia University (Malaysia), Hokkaido University (Sapporo), and has had research collaborations with DFKI (German Research Centre for Artificial Intelligence), NICTA (National ICT Australia), RWC (Real World Computing project, Japan), ICOT (Institute for New Generation Computing, Japan), NII (National Institute for Informatics, Tokyo), and is actively involved in academic and industrial collaborative research projects in Canada, Australia, Europe, and China.

Dekang Lin

U.S. Google Inc.

Topic:Mining Lexical Knowledge from Unlabeled Text.

Summary:There has been a great deal work on acquiring lexical knowledge from unlabeled text, by using textual patterns to extract properties of words or phrases or relationships between them. Many types of lexical knowledge can be obtained this way, including the gender and animacy properties of words, type-instance relationships, part-of relationships, etc. In this talk, I will discuss several examples of this approach and address a number of related issues, such as ``What constitute good patterns?'', ``Where do they come from?'' and 'How to deal with the inherent noise in the extracted instances?''

Resume:Dekang Lin is a Staff Research Scientist at Google. Dekang Lin received his BSc from Tsinghua University and his PhD from the University of Alberta, both in Computer Science. Before joining Google in 2004, he was a professor at the University of Alberta. His main research interests include principle-based parsing and unsupervised learning from text and question-answering. He is Co-Editor-In-Chief of the Transactions of the Association for Computational Linguistics and was General Chair for ACL 2013 and Program Co-chair for ACL 2002.

Li Deng

U.S. Microsoft Research

Topic:Deep Learning: From Academic Concepts to Industrial Triumph.

Summary:Deep learning is a sub-field of machine learning that focuses on hierarchical representations of features or concepts, where high-level semantic-like features can emerge via automatic layer-by-layer learning from low-level features. In recent years, deep learning has achieved important successes in a variety of applied artificial intelligence tasks including speech recognition, computer vision, and natural language processing. The implications of such recent work have been prominently covered in recent media (e.g., NYT, Economist, MIT Technology Reviews, Google acquisition of DNNResearch, etc.). Since 2009, in partnership with leading academic researchers, Microsoft Research has been pursuing deep learning research and technology transfer, and has pioneered the development of industry-scale deep learning technology for speech recognition and other applications, resulting in industry-wide adoption of deep learning in Windows Phones (Microsoft), Android Phones (Google), iPhones (Siri of Apple and Nuance/IBM), and Baidu Phones. In this lecture, I will provide a historical overview on how academic conceptualization of deep learning rapidly evolved into wide product deployment worldwide within only a few short years, and discuss what implications this recent triumphant history may have for future academic-industrial collaborations. I will also go into some technical depth in describing the current deep learning technology, and in particular the disparate approaches which industry and academia take in current pursuits of the technology. I will conclude by analyzing future directions of deep learning, and speculating on what types of information processing and artificial intelligence applications may benefit most from deep learning technology in light of the known mechanisms of human brain that grounds intelligence and extreme effectiveness in information processing.

Resume:Li Deng received the Ph.D. degree from the University of Wisconsin-Madison. He was an assistant professor (1989-1992), tenured associate professor (1992-1996), and Full Professor (1996-1999) at the University of Waterloo, Ontario, Canada. In 1999, he joined Microsoft Research, Redmond, WA, where he is currently a Principal Researcher. Prior to MSR, he also worked or taught at Massachusetts Institute of Technology, ATR Interpreting Telecom. Research Lab. (Kyoto, Japan), and HKUST. He has been granted over 60 US or international patents in acoustics/audio, speech/language technology, and machine learning. He received numerous awards/honors bestowed by IEEE, ISCA, ASA, and Microsoft. In the general areas of audio/speech/language technology and science, machine learning, and signal/information processing, he has published over 300 refereed papers in leading journals and conferences and 4 books. He is a Fellow of the Acoustical Society of America (ASA), a Fellow of the IEEE, and a Fellow of the International Speech Communication Association (ISCA). He served on the Board of Governors of the IEEE Signal Processing Society (2008-2010), and as Editor-in-Chief for the IEEE Signal Processing Magazine (2009-2011). He serves as General Chair of the IEEE ICASSP-2013, and as Editor-in-Chief for the IEEE/ACM Transactions on Audio, Speech and Language Processing. He initiated the deep learning work within Microsoft in 2009 (working with Prof. Geoff Hinton “in house”), with its inspiration and influence soon spread to the industry. His technical work and the leadership since 2009 in industry-scale deep learning with colleagues and academic collaborators have created significant impact in speech recognition and the related areas of information processing including information retrieval, spoken language understanding, speech synthesis, image recognition, machine translation, and web search.

Heng Ji

Computer Science Department

题目:Mining Lexical Knowledge from Unlabeled Text.

摘要:Recent years have witnessed a big data boom that includes a wide spectrum of heterogeneous unstructured data types, from image, speech, and multimedia signals to text documents. Information Extraction (IE) is a task of identifying “facts”, such as the attack/arrest events, people's jobs, people's whereabouts, merger and acquisition activity from such unstructured data sets on a massive scale. Traditional IE techniques assess the ability to extract information from individual documents in isolation. However, users need to gather information which may be scattered among a variety of sources. These facts may be redundant, complementary, incorrect or ambiguously worded. Furthermore, the extracted information from a document may need to augment an existing Knowledge Base (KB). This requires the ability to link events, entities and associated relations in a document to KB entries and thus present many unique challenges. In this talk, I will define several new extensions to state-of-the-art IE and systematically present the foundation, methodologies, algorithms, and implementations needed to create the next generation of information access with more accurate and robust extraction capabilities. More specifically, I will discuss: (1). How to break the performance ceiling of IE by changing the fundamental philosophy from sequential pipeline to joint modeling? (2). How to decode and encode information morphs from noisy data under active censorship? (3). How to construct and populate a trustable KB by marrying IE with data mining?

简历:Heng Ji is the Edward G. Hamilton Development Chair Associate Professor in Computer Science Department of Rensselaer Polytechnic Institute (RPI). She is a faculty member affiliated with three research centers at RPI: Tetherless World Constellation, Rensselaer Institute for Data Exploration and Applications, and Center for Cognition, Communication & Culture. She received her B.A. and M.A. in Computational Linguistics from Tsinghua University in 2000 and 2002 respectively; and her M.S. and Ph.D. in Computer Science from New York University in 2005 and 2007 respectively. Her research interests focus on Natural Language Processing, especially on Cross-source Information Extraction and Knowledge Base Population (KBP). She received Google Research Award, NSF CAREER award, Sloan Junior Faculty award, IBM Watson Faculty award, PACLIC2012 Best Paper Runner-up, "Best of SDM2013" Paper award and AI's Top 10 to Watch award by IEEE Intelligent Systems. She co-organized the NIST TAC-KBP track in 2010 and 2011, and served as the Information Extraction area chair for NAACL2012, ACL2013 and EMNLP2013. Her research is funded by NSF, ARL, DARPA, Google and IBM.

Presentation Instruction

Meeting Places

Below picture is a meeting map which includes meeting places and some other related activities palces.
Move the map and click the green icons for more details

Traffic routes 1:North Station to Kai Lai Hotel

Taxi

specific route:Sun Route，S227，Guanduli Interchange, Wanbao Square, East Central Route，Gan Jiang East Route.
Time：about 25 minutes
Cost：common taxi:about 47 yuan，call for taxi: 80-90yuan（call for taxi，Time:7:00-22:00，Tel:67776777）

Bus

Direct bus：

take 811 Route to Shuangta Station，walk 530m to Kai Lai Hotel.
Time：about 30 minutes
Cost：2 yuan per person

Take bus（the quickest）：

take Express Line 8 Route air conditioning about 21 minutes，to Xiang Cheng Economic Development Zone Station，and take 89 Route about 31 minutes，to Xiangmen Station，240m walk to Kai Lai Hotel.
Time: About 20 minutes
Cost：4 yuan per person

Map：Blue line:taxi，Red line:Direct bus，Green line:Take bus.

Suzhou North Station

taxi entrance

Bus Station

Traffic routes 2:Suzhou Station to Kai Lai Hotel

Taxi

Route1：specific route：Suzhou Station，North Temple Ta，People Route，White Ta North Route，Lingdun Route t oKai Lai Hotel.
Time：about 15 minutes
Cost：about 15 yuan
Route2：specific route：East Merge Route，Tong Fong Bay Bridge，Mo Ye Route，Gan Jiang East Route to Kai Lai Hotel.
Time：about 20 minutes
Cost：about 19 yuan（call for taxi，Time:7:00-22:00，Tel: 67776777）

Bus

Train Station South Square：

Direct 1（Relatively fast）：take 40 Route about 40 minutes to Xiangmen Station，walk 240m to Kai Lai Hotel.
Time：about 40 minutes
Cost：2 yuan per person

Train Station North Square

Direct 2（Relatively fast）walk 170m to Train Station，take 518 about 18 minutes，at Xiangmen Station，walk 240m to Kai Lai Hotel.
Time：30 minutes
Cost：2 yuan per person

Deep blue line: taxi-Route 1，Light blue line: taxi-Route2，pink line: Direct1，Green line: Direct2.

Suzhou Station

Train exit

Red arrow bus exit，Yellow arrow: taxi entrance

Traffic Route 3 Suzhou Park Station to Kai Lai Hotel

taxi:

Route 1：specific route：from Park Station，to He Xi Route，to He Xi Route Elevated，Su Hong Center Route，Bing Lang Route，Su Mu Route，New City Garden，Star Bling Route，Center New Route，Gan Jiang East Route，to Kai Lai Hotel.
Time：about 25 minutes
Cost：about 30 yuan（call for taxi，Time:7:00-22:00，Tel: 67776777）

bus:

No Direct

Take bus:take 139 or 115 about 17 minutes，to administrative center of Park Station West，walk 150m to Star Lake Station，take Subway 1 Route to Xiangmen，walk 240m to Kai Lai Hotel.
Time：about 50 minutes
Cost：5 yuan per person

Blue line:taxi，Green line:bus

Suzhou Park Station

taxi exit

bus station

Red arrow:ticket office

Shang Hai to Suzhou Stop（Beside Suzhou Bank）

lingdun Route Station Subway One Route