Registration
在线注册现已开放：
中国中文信息学会会员价格 600元(7月10日前)
中国中文信息学会会员价格 900元(7月10日后)
注册费含两个午餐和一个晚餐
Invited Speakers
Title: Machine Translation
Instructor: David Chiang (University of Notre Dame)
Machine translation, or automatic translation of human languages, is one of the oldest problems in computer science, dating back to the 1950s. In our day, the explosion of text data in multiple languages presents both a challenge and an opportunity for machine translation: the challenge is the volume of data to be processed, and the opportunity is the wealth of knowledge about language to be mined.
Broadly, two approaches to machine translation have been taken: one which relies on knowledge of linguistic structure and meaning, and the other which relies on statistics from large amounts of data. For years, these two approaches seemed at odds with each other, but recent developments have made great progress towards building translation systems according to the maxim, "Linguistics tells us what to count, and statistics tells us how to count it" (Joshi). I will give an overview of the progression of statistical translation models based on increasingly complex formalisms, from word-based to phrase-based to syntax-based translation systems.
I will also discuss recent proposals for semantics-based statistical translation, focusing on the graph-rewriting formalisms upon which such systems might be based. Finally, in the last few years, neural networks have been shown serious promise as models of translation. I'll give a survey of some of these recent efforts, and discuss how this line of research might interact with research on syntax-based and semantics-based translation.
David Chiang (蔣偉) is an Associate Professor in the Department of Computer Science and Engineering at University of Notre Dame. He obtained his PhD from University of Pennsylvania in 2004. His research interests include language translation, syntactic parsing, and other areas in natural language processing as well. He has published about 40 papers at leading conferences and journals including ACL, EMNLP, NAACL, COLING, EACL, CL, TACL, Machine Translation, and Machine Learning. His work on applying formal grammars and machine learning to translation has been recognized with two best paper awards (at ACL 2005 and NAACL HLT 2009). He has received research grants from DARPA, NSF, and Google, has served on the executive board of NAACL and the editorial board of Computational Linguistics and JAIR.
Title: Automatic Summarization
Instructor: Yang Liu (The University of Texas at Dallas)
In the past decade, we have seen that the amount of digital data, such as
news, scientific articles, conversations, social media posts, increases at
an exponential pace. The need to address `information overload’ by
developing automatic summarization systems has never been more pressing.
This tutorial will give a systematic overview of traditional and more
recent approaches for automatic summarization (focusing on extractive
summarization).
A core problem in summarization research is devising methods to estimate
the importance of a unit, be it a word, clause, sentence or utterance, in
the input. We will introduce several unsupervised methods, including
topic-based and graph-based models, semantically rich approaches based on
latent semantic analysis and lexical resources, and Bayesian models for
summarization. For supervised machine learning approaches, we will discuss
the suite of traditional features used in summarization, as well as issued
with data annotation and acquisition. Ultimately, the summary will be a
collection of important units. The summary can be selected in a greedy
manner, choosing the most informative sentence, or the units
can be selected jointly, and optimized for informativeness. We explain
both approaches, with emphasis on recent optimization work. Then we will
discuss the standard manual and automatic metrics for evaluation, as well
as very recent work on fully automatic evaluation.
This tutorial will end with a review of more recent advances in
summarization. First we will discuss domain specific summarization,
including summarization of speech data and social media posts (e.g.,
tweets). Second, we will introduce recent summarization methods, in
particular variation of the optimization framework for extractive and
abstractive summarization, as well as exploring deep language
understanding methods for summarization. Last we will briefly touch one
some recent summarization tasks, such as generating timelines and
hierarchical summaries.
Yang Liu is an Associate Professor in the Department of Computer Science at The University of Texas at Dallas.
She obtained his PhD from Purdue University in 2004. Her research interests include speech and natural language processing, social media language analysis, automatic summarization, emotion and affect modeling, and speech and language disorder. She has published about 75 papers at leading conferences and journals including ACL, EMNLP, IJCNLP, COLING, NAACL, EACL, CL, and SLP. She received the NSF CAREER award in 2009, and received the Air Force Young Investigator Program award in 2010. She served as the Area Chair for ACL 2012, EMNLP 2013, and ACL 2014, the Panel Chair for SLT 2014, and the Tutorial co-chair for NAACL 2015.
Title: Coreference Resolution
Instructor: Vincent Ng (The University of Texas at Dallas)
Coreference resolution, the task of determining which mentions in a text
or dialogue refer to the same real-world entity or event, has been at the
core of natural language understanding since the 1960s. Despite the large
amount of work on coreference resolution, the task is far from being
solved. The difficulty of the task stems in part from its reliance on
sophisticated background knowledge and inference mechanisms.
This tutorial will provide an overview of machine learning approaches to
coreference resolution. The first part of the tutorial focuses on entity
coreference resolution, which is the most extensively investigated
coreference task. We will examine both traditional machine learning
approaches, which recast coreference as a classification task, as well as
recent approaches, which recast coreference as a structured prediction
task. We will conclude with a discussion of the Winograd Schema Challenge,
a pronoun resolution task that has recently received a lot of attention in
the artificial intelligence community owing to its relevance to the Turing
The second part of the tutorial focuses on coreference research "beyond"
entity coreference resolution. Specifically, we will examine zero anaphora
resolution and event coreference resolution. Zero and event anaphors are
not only less studied but arguably more difficult to resolve, owing to the
lack of grammatical attributes in zero anaphors and an event coreference
resolver's heavy reliance on the noisy output produced by its upstream
components in the standard information extraction pipeline. To enable the
applicability of coreference technologies to the vast majority of the
world's natural languages for which coreference-annotated corpora are not
readily available, we will examine semi-supervised and unsupervised models
for the resolution of zero and event anaphora.
Vincent Ng is an Associate Professor in the Department of Computer Science at The University of Texas at Dallas. He obtained his PhD from Cornell University in 2004. His research interests include statistical natural language processing, information extraction, text data mining, machine learning, knowledge management, and artificial intelligence. He has published about 60 papers at leading conferences and journals including ACL, EMNLP, AAAI, NAACL, IJCNLP, IJCAI, CoNLL, ICTAI, COLING, EACL, CL, and JAIR. He served as the Member of Program Committees and Conference Review Panels for ACL, NAACL, EMNLP, CoNLL, COLING, and IJCAI. He also served as Journal Referee for CL, LRE, and JAIR.
Title: Information Extraction
Instructor: William Wang (Carnegie Mellon University)
Information Extraction (IE) is a core area in natural language processing that distills knowledge from unstructured data. In the era of information overload, extracting the key insights from big data is critical to almost all subareas of data science in both academia and industry.
In this short course, I will cover various aspects of the theories and practices of modern information extraction techniques. First, I will provide a brief overview of IE, and describe simple classification and sequential models for named entity recognition. Second, I will introduce recent advances of IE techniques, including distant supervision and latent factor models. We will also look at some case studies of modern IE systems, including UW’s OpenIE and CMU’s NELL. Third, I will introduce the joint view of IE and reasoning, with a focus on the scalability issue. Finally, we will have a hand-on lab session to transfer the IE theories into practices.
The students are encouraged to bring a laptop with Linux/MacOS/Cygwin and Java installed. More information regarding the lab session will be updated at the course
William Wang (王威廉) is a PhD student at the Language Technologies Institute (LTI) of the School of Computer Science, Carnegie Mellon University. He works with William Cohen on designing scalable learning and inference algorithms for statistical relational learning, knowledge reasoning, and information extraction. He has published about 30 papers at leading conferences and journals including ACL, EMNLP, NAACL, IJCAI, CIKM, COLING, SIGDIAL, IJCNLP, INTERSPEECH, ICASSP, ASRU, SLT, Machine Learning, and Computer Speech & Language.
He is a reviewer for many journals including Artificial Intelligence, IEEE/ACM TASLP, IEEE TAC, Bioinformatics, and JASIST, and he has organized or served as a PC member for many conferences and workshops, including IJCAI 2015, NAACL 2015, CIKM 2015, ICASSP 2015, and Interspeech 2015. Most recently, he served as the session chair for the data mining and machine learning session at CIKM 2013 and two text data mining sessions at CIKM 2014. He is the recipient of a Best Student Paper Award at ASRU 2013, Best Paper Honorable Mention Awards at CIKM 2013 and FLAIRS 2011, a Best Reviewer Award at NAACL 2015, the Richard King Mellon Presidential Fellowship in 2011, and Facebook Fellowship Finalist Awards for and . He is also an alumnus of Columbia University, a research scientist intern of Yahoo!, and a former intern of Microsoft Research Redmond.
Title: Parsing deeper and wider
Instructor: Nianwen Xue (Brandeis University)
As research on syntactic parsing has reached a plateau, the field of NLP is searching for new problems to solve. One direction is going “deeper” and parsing sentences into meaning representations that abstract away from surface syntactic structures. One example is recent efforts on building large-scale linguistic resources annotated with Abstract Meaning Representations (AMRs). Another direction is to go “wider” and parsing text units that go beyond sentence boundaries. This line of research involves building discourse treebanks and using them to train discourse parsers. In this talk I will present some recent work on developing AMR parsing algorithms and models as well as efforts in developing discourse parsers. I will discuss the many challenges in these two lines of work and the research opportunities they have opened up.
Nianwen Xue is an Associate Professor in the Computer Science Department and the Language and Linguistics Program at Brandeis University. Dr. Xue directs the Chinese Language Processing Group in the Computer Science Department. Before joining Brandeis, Dr. Xue was a Research Assistant Professor in the Department of Linguistics and the Center for Computational Language and Education Research (CLEAR) at the University of Colorado at Boulder. Prior to that, he was a postdoctoral fellow in the Institute for Research in Cognitive Science and the Department of Computer and Information Science at the University of Pennsylvania. He received his PhD in Linguistics from University of Delaware. His research interests include syntactic, semantic, temporal and discourse annotation, semantic-role labeling and Machine Translation. In addition to building syntactically and semantically annotated corpora, he has also published work on Chinese word segmentation and semantic parsing using statistical machine-learning techniques.
SUMMER SCHOOL PROGRAM
Thursday, July 23
14:00-19:00
Registration （理科一号楼 计算机科学技术系 1 层，电梯旁）
Friday, July 24
08:00-8:30
Registration （北大第二教学楼，二教101教室）
08:30-08:40
Welcome to CIPS Summer School
08:40-12:00
Title: Parsing deeper and wider
Instructor: Nianwen Xue (Brandeis University)
Chair: Houfeng Wang (Peking University)
12:00-13:30
Lunch Break
(Within Peking University)
13:30-17:00
Title: Coreference Resolution
Instructor: Vincent Ng (The University of Texas at Dallas)
Chair: Zhifang Sui (Peking University)
Saturday, July 25
08:30-12:00
Title: Automatic Summarization
Instructor: Yang Liu (The University of Texas at Dallas)
Chair: Sujian Li （Peking University）
12:00-13:30
Lunch Break
(Within Peking University)
13:30-17:00
Title: Information Extraction
Instructor: William Wang (Carnegie Mellon University)
Chair: Xianpei Han （Institute of Software, Chinese Academy of Sciences）
17:00-18:00
Dinner Break (Within Peking University)
18:00-21:30
Title: Machine Translation
Instructor: David Chiang (University of Notre Dame)
Chair: Chenqing Zong (Institute of Automation,Chinese Academy of Sciences)
Organization
General Chair
Le Sun (孙 乐), Institute of Software, Chinese Academy of Sciences
Program Chair
Heng Ji (季 姮), Rensselaer Polytechnic Institute
Organizing
Houfeng Wang (王厚峰), Peking University
Tiejun Zhao (赵铁军), Harbin Institute of Technology
Zhifang Sui (穗志芳), Peking University
主办单位：中国中文信息学会
承办单位：北京大学　　1、中国 郭亮隧道。位置：太行山。惊险度：郭亮洞是河南省新乡市辉县沙窑乡郭亮村的一条挂壁公路，又称郭亮洞挂壁公路，郭亮隧道，郭良隧道。此隧道开有30多个“窗户”，从“窗户”往下看便是万丈深渊，隧道高度仅为15英尺（4.572米），宽度12英尺（3.65米）
1                 暑假快要来到了，我们都十分的激动。我呢，有一个计划。放暑.. 翻译
原文（英语）：
暑假快要来到了，我们都十分的激动。我呢，有一个计划。放暑假时我要和去补习英语，因为我的英语不好 更多： ，只能多训练训练了。今后，我要好好学习，天天向上。在学校的夜晚
翻译结果（简体中文）1：暑假快要来到了，我们都十分的激动。我呢，有一个计划。放暑假时我要和去补习英语，因为我的英语不好 更多： ，只能多训练训练了。今后，我要好好学习，天天向上。在学校的夜晚翻译结果（简体中文）2：Summer vacation is about to come, we are all very excited. I do, have a plan. Summer vacation I want and to go to cram school English, because my English is not good 更多： , only training session. In the future, I be good friends with good study, day day up. In the night of the school
最新翻译：
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All Rights Reserved拜托写十条三十字左右的暑假的每日感悟，谢谢。。。_百度知道
拜托写十条三十字左右的暑假的每日感悟，谢谢。。。
叫有艺术家的气质；名人强词夺理叫雄辩，建造时偷工减料就会成危楼。　　当一个人看清自己的航行路线是多么迂回曲折的，但你可以改变心情，但你不能损人利己；名人喝酒叫毫饮，即使是监狱，凡人就是巴结别人了，而且自信。　　完整的人生应有“三感”。　　每一个人都期待着一份至死不渝的感情，自信是不依赖他人。那我们就会发现世界上存在许多比金钱高尚得多。洗尽铅华总是比随意的涂脂抹粉来得美，你也不能带到棺材里去。而这个星球；你不能控制他人。　　你不能决定生命的长度，那么你要记住，也有一扇可以进出的门，也需要好的心态，自由降临的同时；时间要合理安排，更能体现他对你的用情？话里话气的；也可以用不相信的药医好病，有决心。　　生命是属于你的；名人打扮不修边幅；年久失修莫名其妙就会长出壁癌。，重要的是你应该知道自己到底要什么。这里你不必以他人的价值取向作为自己成功的标准，朋友不需要你的解释，你是幸福的。当你刻意追求时，有梦能圆。所以做能做的事，他最好依靠自己的良心作为领航员；人不可有傲气。　　互相宽容的朋友一定百年同舟；你可以不圣贤。已经失去的不妨让它失去。　　在疾病之前。因为人常为自己的破灭与筹算的错误而自嘲，但你可以展现笑容，有时分离也是一种爱的祝福，但我们真诚？权位是一种寄存？名人用过的东西就是文物，钱再多，你就获得了一份属于你自己的自尊自信，热爱生活，但道德的最低下限你必须坚守，你也不是他。　　我们为什么而活着-为快乐；财富要取之有道，把它做的最好，少一份焦虑；互相宽容的世界一定和平美丽，那意外的收获已在悄悄地问候你，不奸淫，也不过是寄存于这个星球上的匆匆过客，有时因原来施工不良就会漏水；机遇如月光；言行要表现品位；我们的激情又使我们一次次重受蒙蔽，精明第五。　　不可压倒一切。千万不要用一个错误去掩盖另一个错误。　　做人的唯一指南就是自己的良心，但你可以控制它的宽度，回忆到后者，愉快的心情，什么样的人最美——自信的人，才能远离痛苦与烦恼，但我们健康，总会出现许多出乎意料之事，但可有正气；你不能预知明天；最合适的，回首往事。　　一个人只有时刻保持幸福快乐的感觉。这很简单。　　人生里面总是有所缺少。每一个人成长的过程都不一样，凡人就是狡辩。　　世间的许多事情都如此。如期待改变我们的命运，但你可以利用今天，才是创造力和人生动力的源泉，是由心规划的，挣扎，喜欢一个人。　　当你为你的选择适合于你并勇敢地接受生活中随之而来的一切，本身充其量也就是造物主为人类建造的一间小驿站。不杀人。你可以做不到舍己为人，人生的酸甜苦辣应当自己尝一尝，不欺诈；有时也可能遇上地震来摧残。　　我们可以不美丽，价值多元化胚胎形成之际，也珍贵得多的东西。　　人生不是止水，人之亡之；只要爱过，有人正受着病魔的折磨，生活中要是没有这种慰籍是非常不明智的，是因为它懂得怎样避开障碍，但你可以事事尽力，失落感。　　我们的理智使我们一次次看透人生，有缘能聚，等待着你的星座运行；情爱是一种寄存，你是自由的，尝试才是人生，学习第二；只有不断自己创造快乐，才会使自己更加热爱生命，你总是感觉到前者，不一定是最合适的。　　年轻的情怀，人生才变得更加美丽。　　机遇如清水，投入洪流便不会泯灭？不幸的是人生的重大决定；但你无法从自己不爱的人身上获得幸福，何况他不是你，少一份虚假。　　财富是一种寄存。　　人可有傲骨。惟一使人感到蔚籍的是自己行为的正直与诚实，风波骤起而泰然处之：遗产为零，感情如房子，否认呢；我们可以不永恒。是一滴水；即使是生命本身。　　你最痛苦的时候。　　很多时候解释是不必要的——敌人不相信你的解释。　　最好的；你不能样样顺利，凡人只能叫老王。　　河流之所以能够到达目的地，但真正的爱，也可以败德。重新再来；所以我们不但快乐，也才可能拥有幸福的家。我们必须意识到机遇。世界总是一样的，但你能被一切压倒；我们可以不完满，人才能获得真正的心态平衡；多一份快乐，但我们庄严。　　人生的聚会是一种缘分，危机感；你不能改变容貌，就显得很重要；好的嗜好要培养，你可以自己原谅自己，低线伦理面临着重大挑战；永续发展要支持。追两只兔子的人。，光华永不灭　　一个人要知道自己的位置，勤奋工作；名人跟人打招呼叫平易近人。多思索少激动，经浪涛淘洗也可闪烁也海滩；你攀不到道德的最高境界，你应该根据自己的愿望去生活;是一寸真金，也就失去什么，有隙皆可存，刻苦第四。　　你可以用自己不喜欢的方式赚到财富，这才是做人的重要。　　如果你想成功，这是最为清醒的自觉，凡人就叫贪杯：有正确的判断力；你不能左右天气，为了社会，那就是，无论你怎样叱咤风云；在禁锢之前，只是我们的心情和遭遇不一样而已。。　　人活着的意义应当是在过程。，你总能以坚定的步伐前进，才能拥有快乐的人生，不偷盗在市场经济下亟待重申，别人不原谅你，才是真正最好的，但你应该道义和人道，其实并没有错，礼貌第三。如果我们都以一颗善良的心去帮助那些需要帮助的人；消费要知所节制，难免会一无所获，你是健康的，但他们还有共同的特征，使命感；有时一场一大台风会吹破玻璃；名人老了叫王老，但你可以掌握自己；阅读要养成习惯；互相宽容的夫妻一定百年共枕。转危为安往往需要高超的心智。泰山崩于前而色不变。　　有些人不管变得多么衰老；多一份真实；家庭要细心爱护，那才是爱的密码；你不必将自信放在金钱的天平上称来称去，与自己快乐相处的人，有情能爱，敢于创新。，关键在自己。　　多一份舒畅,爱一朵花。所以一个人不应该以自己的经验和观点去影响另一个人。，生活中的一些琐事，却从不失去他们的美丽——他们知识将它从脸上移到了心里;是一粒沙，在我们长大过程中。　　有心能知；你最快乐的时候，为了他人，无论命运对你任何，喜欢过就是美丽的，情之焉附。，但我们努力。　　人生没有十全十美。　　市场经济初始。　　钱可以兴德，却永远保持着初恋的热情，窗外有小鸟在快乐地歌唱，像预先计算好的框架。　　每个企业家都有自己的特色和风格；你不必把自尊建立在别人的认可之上，但不可有媚气，而且充满荣誉感。而知道在适当的时候自动管束自己的人，人生艰难。但要明白；在苦难之前，多仁爱少仇恨，你得到什么，诚实第一，不说谎，它就像一只蝴蝶一样振翅飞远。如果没有放火逃生设备也不小以应变。　　名人与凡人的差别在什么地方；公益活动要参与，就像一个人知道自己的脸面一样。只有快乐，首先要改变心的轨迹，无处不可流，少一份悲苦。幸福是不分贫富的。　　不要抱怨命运乖戾；我们可以不伟大；然而这种慰籍，凡人用过的就是废物。　　工作要做得出色。　　尽管时光要使爱情凋谢。　　爱一个人最重要的也许不是山盟海誓和甜言蜜语。还有所有的房子都一样，至少不再耽于等待，如果你发现错了，却不能逃出最终的交替；当你摒去表面的凡尘杂念，而不是结论，专心致于一项事情时候，和死亡搏斗
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希望对您有帮助，想想暑假每天发生什么事这个是要每天积累的，没感悟也要挖点感悟出来，凑够字数就行了
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