FISER: An effective method for detecting interactions between topic persons

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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 an interaction network of topic persons. In this paper, we define interaction detection as a classification problem. The proposed interaction detection method, called FISER, exploits nineteen features covering syntactic, context-dependent, and semantic information in text to detect interactive segments in topic documents. Empirical evaluations demonstrate the efficacy of FISER, and show that it significantly outperforms many well-known Open IE methods.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings
Number of pages11
Volume7675 LNCS
Publication statusPublished - 2012
Externally publishedYes
Event8th Asia Information Retrieval Societies Conference, AIRS 2012 - Tianjin, China
Duration: Dec 17 2012Dec 19 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th Asia Information Retrieval Societies Conference, AIRS 2012


  • Information extraction
  • Interactive segment
  • Topic person interaction

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science


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