FISER: A Feature-Based Detection System for Person Interactions

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

研究成果: 雜誌貢獻文章同行評審

摘要

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.
原文英語
頁(從 - 到)656-679
期刊Computational Intelligence
33
發行號4
DOIs
出版狀態已發佈 - 11月 2017

ASJC Scopus subject areas

  • 計算數學
  • 人工智慧

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