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FISER: A Feature‐Based Detection System for Person Interactions
Author(s) -
Chang YungChun,
Chuang PiHua,
Chen Chien Chin,
Hsu WenLian
Publication year - 2017
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12108
Subject(s) - computer science , construct (python library) , feature (linguistics) , set (abstract data type) , artificial intelligence , precision and recall , natural language processing , tuple , relation (database) , recall , feature extraction , context (archaeology) , information retrieval , data mining , mathematics , paleontology , philosophy , linguistics , discrete mathematics , biology , programming language
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 F 1 ‐score of the best feature combination are 72.9%, 55.8%, and 63.2%, respectively.