SMP 2018 特邀报告


特邀讲者

北京语言大学语言资源高精尖创新中心  李宇明  主任


报告主题:世界知识的中文表达问题

报告摘要:语言是知识的容器,也是不同语言的人获取知识的藩篱。知识可以分为现代知识和过往知识。过往知识各种语言都有储存,而现代知识主要储存在现代较为活跃的语言中。现代知识可分为科学知识和社会知识两大类,随着科技的发展、教育普及和科技成果快速地用于社会生活,科学知识成为现代知识的主要代表。随着语言全球化的进程,英语成为“世界通用语”,特别是成为科技(包括社会科学和人文科学)的主要“国际用语”。中文虽然伴随着中国国力的提升,也加快了国际传播的步伐,但是在国际科学技术领域却同英语之外的其他语言一样,被严重边缘化。这将对国人获取世界科技知识(亦即现代知识)形成严重障碍,将对中华文化的发展强盛带来严重影响。虑今思远,我们不能不讨论世界知识的中文表达问题。

嘉宾简介:李宇明,北京语言大学语言资源高精尖创新中心主任。国际中国语言学会会长(2016年-2017年),中国语言学会语言政策与规划专业委员会会长,中国中文信息学会副理事长。曾任国家语委副主任、教育部语言文字信息管理司司长、教育部语言文字应用研究所所长、华中师范大学副校长、北京语言大学党委书记。 1991年获霍英东教育基金奖;1993年破格晋升教授;1994年成为“湖北省有突出贡献的中青年专家”,并享受国务院发放的政府特殊津贴;1996年被评为湖北省劳动模范,全国妇联和国家教委授予“全国优秀家长”称号;1997年获全国“五一”劳动奖章;1998年确定为湖北省跨世纪学术带头人;2000年成为教育部“人文社会科学跨世纪优秀人才”; 2010年应邀为香港中文大学“王泽森-新法书院语文教育访问教授”;2012年获“第八届全国五好文明家庭标兵户”称号; 2013年11月荣膺香港理工大学“杰出中国学人”荣誉。主要研究领域为理论语言学、语法学、心理语言学和语言规划学。出版《儿童语言的发展》《汉语量范畴研究》《语法研究录》《中国语言规划论》《Language Planning in China》《当代中国语言学研究》(主编)《语言学习与教育》《李宇明语言传播与规划文集》等著作20余部;发表论文490余篇,被译为蒙、藏、维、日、法、英、俄、韩等多种文字;主编《全球华语词典》《全球华语大辞典》;曾主持《通用规范汉字表》的研制;与李嵬联合主编的THE LANGUAGE SITUATION IN CHINA(VOLUME1~3)由德国DE GRUYTER出版社与中国商务印书馆联合出版,并有日语、韩语译本。国际著名语言规划学家斯波斯基,高度评价《Language Planning in China》:“这本译文集提供的不仅仅是数据。它通过揭示一位学者,同时也是一位长期活跃的管理者的观点,形成了理解中国语言管理的基础,也为其他地方从事类似工作的人们提供了一个有用的模型。……李宇明这本全新的译文集,已经超越了中国语言规划的主题,可以为指导整个语言管理领域的研究打下坚实的基础。”

新南威尔士大学  林学民  教授


报告主题:Towards Big Graph Processing: Applications, Challenges, and Advances

报告摘要:Graph data are key parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications. Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analysing graph data. In this talk, I will cover various applications, challenges, and recent advances. We will also look to the future of the area.

嘉宾简介:Xuemin Lin is a UNSW Scientia Professor , the head of database group in the school of computer science and engineering at UNSW, and a current Professor at ECNU (specially appointed by the Chinese National Thousands Distinguished Professors Program). He is a fellow of IEEE. Xuemin's research interests lie in databases, data mining, algorithms, and complexities. Specifically, he is working in the areas of scalable processing and mining of various data, including graph, spatial-temporal, streaming, text and uncertain data. Xuemin has been very frequently serving as a PC member and area chairs (senior PC members) in SIGMOD, VLDB, ICDE, ICDM, KDD, CIKM, and EDBT. He received the honour of outstanding reviewer in KDD2012. He was an associate editor of ACM TODS (2008-2014) and IEEE TKDE (Feb 2013- Jan 2015), and an associate editor-in-Chief of TKDE (2015-2016), respectively. Currently, he is the editor-in-Chief of TKDE (Jan 2017 - now) and an associate editor of WWW Journal (2013 - now).

大连理工大学计算机科学与技术学院  林鸿飞  教授


报告主题:幽默计算探讨

报告摘要:幽默作为一种特殊的语言表达方式,是生活中活跃气氛化解尴尬的重要元素。随着人工智能的快速发展,如何利用计算机技术识别和生成幽默成为自然语言处理领域热门的研究内容之一,并逐渐形成一个新兴研究领域,即幽默计算。幽默计算致力于利用计算机手段,理解和识别包含幽默的文本表达,挖掘幽默表达潜在的语义内涵,构建面向幽默表达的计算模型,并在此基础上,智能地将非幽默表达转化为幽默表达,实现幽默的自动生成,提升人机交互的智能程度。

本次报告首先对当前幽默计算的背景进行概述,阐明幽默的可计算性;在此基础上,对幽默研究的发展情况进行回顾,梳理幽默研究的认知语言学基础理论;然后,综述当前幽默计算在幽默识别和幽默生成两个方面的进展情况,给出幽默计算的研究框架,从幽默的表达方式和呈现载体两个维度讨论幽默的识别和生成,主要讨论谐音、双关语的识别与生成、相声和情景喜剧的结构分析。最后,对幽默计算在聊天机器人、机器翻译、外语教学等多个自然语言处理任务上的应用前景。希望通过对幽默计算及其应用研究的总结,完善现有幽默计算模型,增进计算机对于自然语言的理解,推动人工智能的进一步发展。

嘉宾简介:林鸿飞,大连理工大学计算机科学与技术学院教授、博士生导师。主要研究领域为自然语言处理、情感计算、社会媒体处理、面向生物医学领域的文本挖掘。担任中国中文信息学会常务理事,中国中文信息学会社会媒体处理专业委员会副主任、信息检索专业委员会常务委员,中国人工智能学会离散智能计算专业委员会副主任、机器学习专业委员会常务委员,辽宁省高等学校计算机专业教学指导委员会副主任、辽宁省计算机基础教育学会理事长。《中文信息学报》编委、《模式识别与人工智能》编委、《小型微型计算机系统》编委、《大连理工大学》(自然科学版)编委,SCI期刊IJDMB编委。主持国家863高科技计划、国家自然科学基金和教育部博士点基金等项目十余项,参与国家自然科学基金重点项目、国家十二五支撑计划项目多项。SCI收录论文80余篇,在SIGIR、ACL、CIKM和BIBM等国际重要学术会议发表论文20余篇,入选辽宁省“百千万人才工程”百人层次。获得国家级教学成果一等奖、二等奖和辽宁省教学成果一等奖。

北京师范大学新闻传播学院  张洪忠  教授


报告主题:异类还是共生:社交网络中人与AI的关系探讨

报告摘要:本文基于文献梳理,指出当前AI机器人研究的两个特点:一是计算机科学家们更多从技术角度出发,关注AI机器人的识别、排除与制造等问题;二是社会科学研究者则将AI机器人视为虚假的信息源、干扰性的信息内容,看作社交媒体中的噪音和杂质,主张对AI机器人进行批判或予以忽视(即将全部在线痕迹数据均看作真实的人的痕迹)。从本质上看,两类学者们都将AI机器人视为社会网络中的“异类”。本文通过一个社交群组中的仿真实验,模拟真实用户与AI机器人之间的信息交流过程,借此反思社交网络中人与AI机器人的关系,提出研究者应正视AI机器人普遍存在的现实,逐步发展“人机共生”语境下的分析视角。

嘉宾简介:张洪忠,北京师范大学新闻传播学院教授,广电总局“新闻出版大数据用户行为追踪与分析实验室”主任。研究方向是传播效果测量、人工智能的媒介应用、传媒公信力。目前在SSCI、CSSCI期刊和中文学术刊物发表六十多篇学术论文,出版了4本专著和参编著作多部。独立承担了一项国家自然科学基金和两项社科基金项目,以及上百项媒介调查项目。

the College of Computing and Informatics, Drexel University  Xiaohua Tony Hu  full professor


报告主题:Question-based Text Summarization

报告摘要:In the modern information age, finding the right information at the right time is an art (and a science). However, the abundance of information makes it difficult for people to digest it and make informed choices. In this talk, we will discuss how to help people who want to quickly capture the main idea of a piece of information before they read the details through text summarization. In contrast with existing works, which mainly utilize declarative sentences to summarize a text document, we aim to use a few questions as a summary. In this way, people would know what questions a given text document can address and thus they may further read it if they have similar questions in mind. A question-based summary needs to satisfy three goals, relevancy, answerability, and diversity. Relevancy measures whether a few questions can cover the main points that discussed in a text document; answerability measures whether answers to the questions are included in the text document; and diversity measures whether there is redundant information carried by the questions.

To achieve the three goals, we design a two-stage approach which consists of question selection and question diversification. The question selection component aims to find a set of candidate questions that are relevant to a text document, which in turn can be treated as answers to the questions. Specifically, we explore two lines of approaches that have been developed for traditional text summarization tasks, extractive approaches and abstractive approaches to achieve the goals of relevancy and answerability, respectively. The question diversification component is designed to re-rank the questions with the goal of rewarding diversity in the final question-based summary. Evaluation on product review summarization tasks for two product categories shows that the proposed approach is effective for discovering meaningful questions that are representative for individual reviews. This research work opens up a new direction in the intersection of information retrieval and natural language processing.

嘉宾简介:Xiaohua Tony Hu is a full professor at the College of Computing and Informatics, Drexel University and lecture professor at Central China Normal University. He is also serving as the founding Co-Director of the USA National Science Foundation Center on Visual and Decision Informatics (NSF CVDI, the only designated NSF Industry-University sponsored Big Data center at USA, and IEEE Computer Society Big Data Conference Steering Committee Chair. Tony is a scientist, teacher and entrepreneur. He joined Drexel University in 2002. Earlier, he worked as a research scientist in the world-leading R&D centers such as Nortel Research Center, and Verizon Lab (the former GTE labs). In 2001, he founded the DMW Software in Silicon Valley, California. He has a lot of experience and expertise to convert original ideas into research prototypes, and eventually into commercial products, many of his research ideas have been integrated into commercial products and applications in data mining fraud detection, database marketing.

Tony’s current research interests are in big data, data/text/web mining, bioinformatics, and social media. He has published more than 270 peer-reviewed research papers in various journals, conferences and books. His research projects are funded by the National Science Foundation (NSF), US Dept. of Education, the PA Dept. of Health, the Natural Science Foundation of China (NSFC). He has obtained more than US$9.5 million research grants in the past 12 years as PI or Co-PI and has graduated 23 Ph.D. students from 2006 to 2017 and is currently supervising 8 Ph.D. students.

中央财经大学国际经济与贸易学院  李兵  副教授


报告主题:经济学中的大数据应用

报告摘要:随着信息技术的普及与广泛应用,数据来源更加广泛,数据的收集成本不断下降,这也为经济学中原本无法检验的理论提供了实证验证的可能。本讲座主要介绍大数据在经济学研究中的应用,包括卫星遥感数据、行政管理数据、网络平台数据、媒体文本数据等在经济学顶级期刊发表文章中的应用实例。并结合本人使用大数据的研究成果讨论研究经验与教训,最后对未来的大数据在经济学研究中的应用进行展望。

嘉宾简介:李兵,中央财经大学国际经济与贸易学院副教授,贸易经济系系主任。2008年于吉林大学获得经济学博士学位,2011年于香港科技大学获得社会科学博士学位。主要研究方向是外商直接投资、国际贸易、国际政治经济学、互联网经济学、贸易与创新等。研究成果发表于The Economics of Transition、《经济研究》、《世界经济》等SSCI英文期刊和中文权威期刊。主持省部级课题2项,国家社科重大课题子课题负责人1项,参与2项,参与教育部重大课题1项,其他省部级课题以及政府部门课题多项。全国万名优秀创新创业导师人才库首批入库导师;中国技术经济学会知识产权分委会理事;《经济研究》、《世界经济》等期刊匿名审稿人;“雏鹰读书会”创始人,学术顾问。