FISER: An Effective Recognizer for Detecting Topic-dependent Interactive Relation
Date Issued
2012
Date
2012
Author(s)
Chuang, Pi-Hua
Abstract
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 inter-sentential and
iv
intra-sentential interactive segments in topic documents. Empirical evaluations demonstrate that FISER outperforms many well-known open IE methods on identifying interactive segments in topic documents. In addition, the precision, recall and F1-score of the best feature combination are 72.6%, 55.6%, and 61.9% respectively.
Subjects
Information extraction
Relation detection
Open IE
Type
thesis
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