SPIRIT: A Tree Kernel-Based Method for Topic Person Interaction Detection

Yung Chun Chang, Chien Chin Chen, Wen Lian Hsu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The development of a topic in a set of topic documents is constituted by a series of person interactions at a specific time and place. Knowing the interactions of the persons mentioned in these documents is helpful for readers to better comprehend the documents. In this paper, we propose a topic person interaction detection method called SPIRIT, which classifies the text segments in a set of topic documents that convey person interactions. We design the rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a tree-based convolution kernel to identify interactive segments. Experiment results based on real world topics demonstrate that the proposed rich interactive tree structure effectively detects the topic person interactions and that our method outperforms many well-known relation extraction and protein-protein interaction methods.

Original languageEnglish
Article number7468551
Pages (from-to)2494-2507
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume28
Issue number9
DOIs
Publication statusPublished - Sep 1 2016
Externally publishedYes

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Keywords

  • classification
  • natural language processing
  • Text mining

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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