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更好地利用人群Making Better Use of the Crowd |
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| 课程网址: | https://videolectures.net/videos/kdd2017_tutorial12_making_better... |
| 主讲教师: | Jennifer Wortman Vaughan |
| 开课单位: | KDD 2017研讨会 |
| 开课时间: | 2017-11-21 |
| 课程语种: | 英语 |
| 中文简介: | 在过去的十年里,众包已被用来利用人类计算的力量来解决仅靠计算机难以解决的任务,例如确定图像是否包含树、评估网站的相关性或验证企业的电话号码。机器学习和自然语言处理社区很早就将众包作为一种工具,用于快速、无休止地获取训练系统所需的大量标记数据。一旦收集到这些数据,就可以将其交给学习做出自主预测或行动的算法。通常,这种切换是与人群互动结束的地方。人群提供了数据,但最终的目标是让人类脱离循环。有没有更好的方法来利用人群?在本教程中,我将展示众包在数据收集之外的创新用途。我还将深入研究最近的研究,旨在了解众包工作者是谁,他们的行为方式,以及这应该教会我们与人群互动的最佳实践。 |
| 课程简介: | Over the last decade, crowdsourcing has been used to harness the power of human computation to solve tasks that are notoriously difficult to solve with computers alone, such as determining whether or not an image contains a tree, rating the relevance of a website, or verifying the phone number of a business. The machine learning and natural language processing communities were early to embrace crowdsourcing as a tool for quickly and inexpensively obtaining the vast quantities of labeled data needed to train systems. Once this data is collected, it can be handed off to algorithms that learn to make autonomous predictions or actions. Usually this handoff is where interaction with the crowd ends. The crowd provides the data, but the ultimate goal is to eventually take humans out of the loop. Are there better ways to make use of the crowd? In this tutorial, I will showcase innovative uses of crowdsourcing that go beyond the collection of data. I will also dive into recent research aimed at understanding who crowdworkers are, how they behave, and what this should teach us about best practices for interacting with the crowd. |
| 关 键 词: | 机器学习; 自然语言处理; 创新用途 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2024-11-29:liyq |
| 最后编审: | 2024-11-29:liyq |
| 阅读次数: | 75 |
