The 13th China National Conference on Computational Linguistics (CCL2014) will take place in Wuhan (http://en.wikipedia.org/wiki/Wuhan), China, 18-19 October 2014, hosted by Central China Normal University (http://english.ccnu.edu.cn/). CCL, an annual conference starting from 1991 (bi-annual before 2013) 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.

Papers submitted to CCL2014 can be in Chinese or English. Paper will be presented orally or as posters are determined by the Program Committee. Accepted Oral papers in Chinese will be published in the Journal of Chinese Information Processing, the most influential Journal in Computational Linguistics in China. CCL will recommend accepted Poster papers to other core Computer Science journals, which might require an additional review before acceptance for publication. Accepted papers in English will be published by Springer in the Lecture Notes in Artificial Intelligence (LNAI) series.

Attached to the 13th CCL, we start up the Second International Symposium on Natural Language Processing based on Naturally Annotated Big Data (2nd NLP-NABD). NLP-NABD covers all the NLP topic s, 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 2014 is supported by National Key Basic Research Program of China (i.e., “973” Program) “Theory and Methods for Cyber-Physical-Human Space Oriented Web Chinese Information Processing” under the grant NO.2014CB340500.

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 13th CCL.