CCF学科前沿讲习班

The CCF Advanced Disciplines Lectures

CCFADL第114期

主题  188体育app官网：

2020年12月20-22日 北京

188体育app官网：作为大数据时代重要的知识表示方式，有效地实现了大规模数据认知与推理，是人工智能逻辑推理、知识与数据融合、认知与计算融合以及复杂协同决策等关键技术发展与应用的重要一环，已经成为人工智能领域的一个重要支撑。

本期CCF学科前沿讲习班《188体育app官网：》，讲授当前188体育app官网：发展关键技术及其最新研究进展，旨在全面系统讲授、研讨188体育app官网：相关主题，对当前188体育app官网：前沿技术与行业落地实践进行系统性介绍，帮助学员系统地掌握188体育app官网：概念与技术，快速了解和学习该领域的研究热点和前沿技术，掌握学科发展动向和重要的应用方法，开阔科研视野，增进学术交流，增强实践能力。

本期讲习班邀请到了本领域6位来自于著名高校与科研机构的重量级专家学者做主题报告。他们将对面向事件的语言理解、认识图谱、基于图神经网络的188体育app官网：、188体育app官网：的表示学习、188体育app官网：的数据管理、及知识指导下的预训练语言模型等关键技术、应用实践及当前热点问题进行深入浅出的讲解，并对如何开展本领域前沿技术研究等进行指导，使参加者在了解学科热点、提高理论水平的同时，掌握最新技术趋势。

学术主任：肖仰华 复旦大学

主办单位：中国计算机学会

活动日程：

特邀讲者：

Heng Ji  UIUC教授

讲者简介：Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department of University of Illinois at Urbana-Champaign. She is an Amazon Scholar. She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge Base Population and Knowledge-driven Generation. She was selected as "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. The awards she received include "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014, Bosch Research Award in 2014-2018 and Tencent AI Lab Rhino-Bird Gift Fund in 2019, and ACL2020 Best Demo Paper Award. She has served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018. She is elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2021.

报告题目：Event-Centric Natural Language Understanding

报告摘要： This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text. These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and properties, as well as external structures concerning memberships, temporal and causal relations of multiple events. This tutorial will provide audience with a systematic introduction of (i) knowledge representations of events, (ii) various methods for automated extraction, conceptualization and prediction of events and their relations, (iii) induction of complex event schemas, and (iv) multi-lingual multimedia common structured semantic space construction for cross-lingual cross-media event structure transfer. We will conclude the tutorial by outlining emerging research problems in this area.