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基于web文章描述方法的人类活动知识自动提取

Automatic Extraction of Human Activity Knowledge from Method-Describing Web Articles
课程网址: http://videolectures.net/akbc2010_myaeng_aehak/  
主讲教师: Sung Hyon Myaeng
开课单位: 韩国科学技术高级研究所
开课时间: 2010-06-07
课程语种: 英语
中文简介:
对于许多可以从上下文感知或活动预测中获益的定制用户服务来说,关于各种领域中的日常人类活动的知识是无价的。过去构建此类知识库的方法都是特定于领域的,且不可伸缩。最近一种从Web资源中提取日常生活活动(ADL)的尝试处理与实现这些活动和对象有关的活动和对象,而不是活动中的操作序列。本文描述了一种从描述在不同领域执行任务的方法的Web文章中自动提取人类活动知识的方法。目标知识库由活动目标、动作和成分组成,这些活动目标、动作和成分是通过基于句法模式和基于概率的机器学习方法提取出来的。对结果进行了精确性和对一些基线的覆盖率的评估。
课程简介: Knowledge on daily human activities in various domains is invaluable for many customized user services that can benefit from context-awareness or activity predictions. Past approaches to constructing a knowledge base of this kind have been domain-specific and not scalable. A recent attempt to extract activities of daily living (ADL) from Web resources deal with activities and objects involved in achieving them but not the sequence of actions in an activity. This paper describes an approach to automatically extracting human activity knowledge from Web articles that describe methods for performing tasks in a variety of domains. The target knowledge base is comprised of activity goals, actions, and ingredients, which are extracted with syntactic pattern-based and probabilistic machine learning based methods. The result is evaluated for accuracy and coverage against some baselines.
关 键 词: Web; 人类活动; 知识自动提取
课程来源: 视频讲座网
最后编审: 2020-06-08:heyf
阅读次数: 19