◆  CCF-NLPCC Distinguished Achievement Award: Prof. Zhiwei Feng

Zhiwei Feng, senior research fellow and professor of computational linguistics, Institute of Applied Linguistics, the Ministry of Education (MOE, PRC).
He was visiting scholar at Institute of Applied Mathematics (IMAG-GETA) at Grenoble University (France, 1978-1981), chief of Machine Translation Group of Institute of Scientific and Technical Information of China (ISTIC, Beijing, 1981- 1985), guest scientist of Fraunhofer-Institute (FhG) at Stuttgart (Germany, 1986-1988), professor of University Trier (Germany, 1990-1993, 1999-2000), academic consultant of CITAL at Konstanz Fachhochschule (Germany, 1996), professor of EECS (Electronic Engineering and Computer Science) department at Korea Advanced Institute of Science and Technology (KAIST, Daejeon, Korea, 2000-2001, 2003-2004).
Now he is appointed professor in many universities in China, including Peking University, Zhejiang University, Communication University of China, Dalian Maritime University, Hangzhou Normal University. He is also member of Standardization Committee of State Language Commission, assessment member of National Philosophy & Social Science Fund, consultant of Trans-European Language Resource Infrastructure (TELRI, EU), consultant of Hong Kong Terminology Association, member of consultant committee of International Language Resources and Evaluation Congress (LREC), editorial board member of International Journal of Corpus Linguistics (IJCL, Amsterdam) , International Journal of Chinese and Computing (IJCC, Singapore), Chinese Language (Zhongguo Yuwei, Beijing), Chinese Science and Technology Terms Journal, (Beijing).
His main academic interests include machine translation, information extraction, mathematical linguistics, quantitative linguistics, computational terminology and basic theory of formal analysis of natural languages. He has published more than 30 monograph books and more than 400 scientific papers.

冯志伟，1939年4月生，计算语言学家，教育部语言文字应用研究所研究员，博士生导师。他曾是格勒诺布尔大学应用数学研究所自动翻译中心访问学者（IMAGGETA，法国，1978-1981），中国科学技术信息研究所机器翻译组组长（ISTIC，北京，1981-1985），夫琅禾费研究院客座研究员（FhG, 德国，斯图加特，1986-1988），特里尔大学教授（德国，1990-1993，1999-2000），康士坦茨应用技术大学术语研究中心学术顾问（CITAL，德国，1996），韩国科学技术院电子工程与计算机科学系教授（EECS，KAIST，韩国，大田，2000-2001，2003-2004）。
他还是北京大学、浙江大学、中国传媒大学、大连海事大学兼职教授，杭州师范大学高端特聘教授，担任国家语委语言文字规范标准审定委员会委员、国家社科基金评审委员、香港术语学会顾问，并担任跨欧洲语言资源基础建设项目顾问（TELRI, 欧盟）、语言资源与评测国际会议顾问（LREC），《语料库语言学国际杂志》（IJCL，阿姆斯特丹）、《中文计算国际杂志》（IJCC, 新加坡）、《中国语文》、《中国科技术语》等刊物的编委，还担任《中文信息学报》顾问。他主要研究机器翻译、信息抽取、数理语言学、计量语言学、计算术语学以及自然语言形式分析的理论和方法，已出版专著30 余部，发表论文400 余篇。

◆  CCF-NLPCC Young Outstanding Scientist Award: Associate Professor Yue Zhang

Yue Zhang is currently an Associate Professor at WestLake University. He works on natural language processing and in particular structured prediction tasks. He has a line of research contributions on syntax, semantics, entity recognition, relation extraction, event detection, structured sentiment etc., and their downstream applications on text mining. Yue Zhang proposed a learningguided heuristic search framework for structured prediction. Compared with traditional methods using dynamic programming, this method decouples feature context and search complexity, thereby avoiding the intrinsic conflict between accuracy and efficiency. It has achieved the stateof-the-art results on a range of tasks in the literature. For example, for syntactic parsing, Yue Zhang’s model ZPar gave higher accuracies and over 15 times faster speeds compared with Berkeley parser and Stanford parser in 2013.
Since 2014, Yue Zhang has worked on using deep learning to enhance the framework, achieving improved results. Thanks to the low computational complexity, the method allows joint models which tackle very complex combined search spaces. Yue Zhang studied joint segmentation, tagging and parsing, joint parsing and semantic role labelling, joint entity and relation/sentiment extraction, and joint morphology prediction and linearisation.
Over the recent years Yue Zhang has investigated the use of deep learning for crossdomain, cross-task, cross-lingual and cross-standard information exploration. The main research goal is to leverage the maximum amount of resources available for robust general domain NLP and information extraction.
Yue Zhang has over 85 articles at CCF A B conferences, with a Google scholar citation over 2450 and H-index 24.

张岳，1980 年9 月生。他目前担任西湖大学副教授。他专注于自然语言处理中的结构预测问题，在中英文词法、句法分析，实体，关系识别，事件抽取，结构情感分析等任务，以及上述任务在文本挖掘的下游应用上做出学术贡献。
首先，张岳提出了一套利用机器学习引导近似搜索的结构预测框架，消除了特征范围和算法复杂度分关联，因此解决了传统动态规划算法精确度与速度之间的内在矛盾，并且在自然语言处理的一系列任务上取得了文献中领先的速度与准确度。比如句法分析，张岳的算法ZPar 在2013 年标准测试集取得了比竞争对手Berkeley 和Stanford parser 更高的准确度和15 倍以上的速度优势。
2014 年以来，张岳研究了上述结构预测框架与深度学习的结合，并且取得了更高的准确度。由于这套框架算法复杂度低，所以在解决联合任务带来的复杂搜索空间挑战上也有优势。张岳研究了分词、词性标注、句法分析，句法分析与语义角色标注，信息抽取中的实体、关系、情感，以及造句任务中的词形变化和句子结构等任务的联合模型。
近年来，张岳专注于利用深度学习技术实现跨领域、跨任务、跨语言以及跨标注规范的信息利用。研究的重点在于融合尽量多的现有资源，达到普遍领域稳定准确的自然语言处理和信息抽取技术。

◆  CCF-NLPCC Young Outstanding Scientist Award: Associate Professor Xu Sun

Xu Sun is Associate Professor and PhD Supervisor in School of Electronics Engineering and Computer Science, Peking University, since 2012. He received Ph.D. from the University of Tokyo in 2010. He worked as Research Fellow/Associate at the University of Tokyo, Cornell University, and Hong Kong Polytechnic University. He also worked at Microsoft Research Redmond as a visiting researcher. His research focuses on natural language processing and machine learning, especially on structural natural language processing and natural language generation. His main contributions include (1) innovations of theories and methods for structured natural language processing and (2) cutting-edge researches on natural language generation based on deep neural networks. He has published over 50 papers on international premier journals and conferences of natural language processing and machine learning, and more than 20 of them are on top journals and conferences including ACL, ICML, and NIPS. He served as Area Chair and Senior Program Committee (SPC) of premier international academic conferences such as EMNLP and IJCAI. He received Qiu Shi Outstanding Young Scholar Award, Qiu Shi Foundation, 2015. He received COLING 2018 Best Paper Award.

孙栩，1983 年10 月生。他于2012 年开始担任北京大学信息学院研究员、博士生导师。2010 年于日本东京大学获得计算机博士学位。先后在日本东京大学、美国康奈尔大学、香港理工大学担任研究职位。曾在微软公司美国雷蒙德研究院访问研究。研究方向为自然语言处理、机器学习，目前关注自然语言的结构化学习、自然语言生成研究。主要成果：1) 面向自然语言的结构化学习理论与方法方面的创新研究；2) 基于深度神经元网络的自然语言生成方面的一系列创新研究。先后在自然语言处理、机器学习领域的国际高水平会议和期刊发表50 余篇论文，包括ACL, ICML, NIPS 等CCF-A 类期刊和会议论文20 余篇。担任EMNLP、IJCAI 等旗舰国际学术会议的领域主席、高级程序委员；长期担任ACL、AAAI 等本领域大部分主流会议的程序委员。2014 年入选中组部第十批“千人计划”青年人才。2015 年获得求是基金会的“求是杰出青年学者奖”。2018 年获得COLING 2018 最佳论文奖。