Social relation extraction from texts using a support-vector-machine-based dependency trigram kernel

  • Authors:

  • Maengsik Choi;Harksoo Kim

  • Affiliations:

  • Program of Computer and Communications Engineering, College of IT, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon-si, Gangwon-do 200-701, Republic of Korea;Program of Computer and Communications Engineering, College of IT, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon-si, Gangwon-do 200-701, Republic of Korea

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2013

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Abstract

We propose a social relation extraction system using dependency-kernel-based support vector machines (SVMs). The proposed system classifies input sentences containing two people's names on the basis of whether they do or do not describe social relations between two people. The system then extracts relation names (i.e., social-related keywords) from sentences describing social relations. We propose new tree kernels called dependency trigram kernels for effectively implementing these processes using SVMs. Experiments showed that the proposed kernels delivered better performance than the existing dependency kernel. On the basis of the experimental evidence, we suggest that the proposed system can be used as a useful tool for automatically constructing social networks from unstructured texts.