人类世界能够赋予的最高学历,一般被视为进入科研领域和学术圈的门槛。

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报告主题: 信息检索

报告摘要: 引入结构化的知识是目前辅助自然语言处理任务的重要方法之一。如何准确地从自由文本中获取结构化信息,以及进行有效的知识表示在近几年取得了广泛关注。在这次报告中,讲者将梳理知识表示与获取的发展脉络,分享相关领域的最新工作进展,报告人将会以他在知识表示与关系抽取上的若干代表工作为例子,对研究中遇到的具体问题进行深入探讨分析,并结合讲者个人的工作经验,讨论如何体系化地开展研究工作以及学术合作等问题,分享其在解决问题的过程中的一些心得体会。

邀请嘉宾: 韩旭 清华大学计算机系17级博士研究生,来自清华大学自然语言处理组,由刘知远副教授指导,主要研究方向为自然语言处理及信息抽取。目前已在人工智能、自然语言处理等领域的著名国际会议ACL,EMNLP,NAACL,COLING,AAAI发表相关论文多篇,在Github上维护开源工程多项。

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最新论文

Computer vision technology is widely used in biological and medical data analysis and understanding. However, there are still two major bottlenecks in the field of cell membrane segmentation, which seriously hinder further research: lack of sufficient high-quality data and lack of suitable evaluation criteria. In order to solve these two problems, this paper first proposes an Ultra-high Resolution Image Segmentation dataset for the Cell membrane, called U-RISC, the largest annotated Electron Microscopy (EM) dataset for the Cell membrane with multiple iterative annotations and uncompressed high-resolution raw data. During the analysis process of the U-RISC, we found that the current popular segmentation evaluation criteria are inconsistent with human perception. This interesting phenomenon is confirmed by a subjective experiment involving twenty people. Furthermore, to resolve this inconsistency, we propose a new evaluation criterion called Perceptual Hausdorff Distance (PHD) to measure the quality of cell membrane segmentation results. Detailed performance comparison and discussion of classic segmentation methods along with two iterative manual annotation results under existing evaluation criteria and PHD is given.

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