Automatic Extraction of Human Activity Knowledge from Method-Describing Web Articles
published: June 7, 2010, recorded: May 2010, views: 249
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Download slides: akbc2010_myaeng_aehak_01.pdf (1.0 MB)
Download article: akbc2010_myaeng_aehak_paper.pdf (237.7 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !