Editorial: Occupation inference through detection and classification of biographical activities

  • Authors:

  • Elena Filatova;John Prager

  • Affiliations:

  • Department of Computer and Information Sciences, Fordham University, 441 East Fordham Road, Bronx NY 10458, United States;IBM T.J. Watson, Research Center, Yorktown Heights, NY 10598, United States

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

Dealing with biographical information (e.g., biography generation, answering biography-related questions, etc.) requires the identification of important activities in the life of the individual in question. While there are activities that can be used in any biography (e.g., person was born on a particular date, person lived in a particular location, etc.), many activities used in biographies tend to be occupation-related, others are person-specific. Hence, occupation gives important clues as to which activities should be included in the biography. In this paper, we present a methodology for identifying a three-level hierarchy of biographical activities: those activities that apply to the general population, those activities that are occupation-related, and those activities that are person-specific. We use the obtained occupation-related activities as features for a multi-class SVM classifier to identify the occupation of a previously unseen individual. We also show that the activities automatically obtained from text can be used as features not only for a classification task but for a clustering task as well. We show that, given the correct number of clusters, people belonging to the same occupation are clustered together. At the same time, clustering people into a smaller number of classes allows the grouping of practitioners of the occupations that share a considerable number of occupation-related activities. Thus, analyzing descriptions of people belonging to various occupations, we can build a hierarchy of occupations.