Human activity mining using conditional radom fields and self-supervised learning

  • Authors:

  • Nguyen Minh The;Takahiro Kawamura;Hiroyuki Nakagawa;Ken Nakayama;Yasuyuki Tahara;Akihiko Ohsuga

  • Affiliations:

  • Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan;Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan;Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan;Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan and Institute for Mathematics and Computer Science, Tsuda College, Tokyo, Japan;Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan;Graduate School of Information Systems, The University of Electro-Communications, Tokyo, Japan

  • Venue:
  • ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
  • Year:
  • 2010

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Abstract

In our definition, human activity can be expressed by five basic attributes: actor, action, object, time and location. The goal of this paper is describe a method to automatically extract all of the basic attributes and the transition between activities derived from sentences in Japanese web pages. However, previous work had some limitations, such as high setup costs, inability to extract all attributes, limitation on the types of sentences that can be handled, and insufficient consideration interdependency among attributes. To resolve these problems, this paper proposes a novel approach that uses conditional random fields and self-supervised learning. This approach treats activity extraction as a sequence labeling problem, and has advantages such as domain-independence, scalability, and does not require any human input. In an experiment, this approach achieves high precision (activity: 88.9%, attributes: over 90%, transition: 87.5%).