FISER: A Feature-Based Detection System for Person Interactions

Yung Chun Chang, Pi Hua Chuang, Chien Chin Chen, Wen Lian Hsu

Research output: Contribution to journalArticlepeer-review


Discovering the interactions between the persons mentioned in a set of topic documents can help readers construct the background of the topic and facilitate document comprehension. To discover person interactions, we need a detection method that can identify text segments containing information about the interactions. Information extraction algorithms then analyze the segments to extract interaction tuples and construct a network of person interaction. In this article, we define interaction detection as a classification problem. The proposed interaction detection method, called feature-based interactive segment recognizer (FISER), exploits 19 features covering syntactic, context-dependent, and semantic information in text to detect intra-clausal and inter-clausal interactive segments in topic documents. Empirical evaluations demonstrate that FISER outperformed many well-known relation extraction and protein-protein interaction detection methods on identifying interactive segments in topic documents. In addition, the precision, recall, and F1-score of the best feature combination are 72.9%, 55.8%, and 63.2%, respectively.

Original languageEnglish
Pages (from-to)656-679
JournalComputational Intelligence
Issue number4
Publication statusPublished - Nov 2017


  • Information extraction
  • Interactive segment
  • Person interaction
  • Relation extraction
  • Text mining

ASJC Scopus subject areas

  • Computational Mathematics
  • Artificial Intelligence


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