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 |
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頁(從 - 到) | 656-679 |
期刊 | Computational Intelligence |
卷 | 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