Chinese Relation extraction by Multiple instance learning

Yu Ju Chen, Jane Yung Jen Hsu

研究成果: 圖書/報告稿件的類型會議稿件同行評審

8 引文 斯高帕斯(Scopus)


Relation extraction, which learns semantic relations of concept pairs from text, is an approach for mining common-sense knowledge. This paper investigates an approach for relation extraction, which helps expand a commonsense knowledge base with little labor work. We proposed a framework that learns new pairs from Chinese corpora by adopting concept pairs in Chinese commonsense knowledge base as seeds. Multiple instance learning is utilized as the learning algorithm for predicting relation for unseen pairs. The performance of our system could be improved by learning multiple iterations. The results in each iteration are manually evaluated and processed to next iteration as seeds. Our experiments extracted new pairs for relations "AtLocation", "CapableOf', and "HasProperty". This study showed that new pairs could be extracted from text without huge humans work.

主出版物子標題Artificial Intelligence Applied to Assistive Technologies and Smart Environments; WS-16-02: AI, Ethics, and Society; WS-16-03: Artificial Intelligence for Cyber Security; WS-16-04: Artificial Intelligence for Smart Grids and Smart Buildings; WS-16-05: Beyond NP; WS-16-06: Computer Poker and Imperfect Information Games; WS-16-07: Declarative Learning Based Programming; WS-16-08: Expanding the Boundaries of Health Informatics Using AI; WS-16-09: Incentives and Trust in Electronic Communities; WS-16-10: Knowledge Extraction from Text; WS-16-11: Multiagent Interaction without Prior Coordination; WS-16-12: Planning for Hybrid Systems; WS-16-13: Scholarly Big Data: AI Perspectives, Challenges, and Ideas; WS-16-14: Symbiotic Cognitive Systems; WS-16-15: World Wide Web and Population Health Intelligence
發行者AI Access Foundation
出版狀態已出版 - 2016
事件30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, 美國
持續時間: 12 02 201613 02 2016


名字AAAI Workshop - Technical Report
WS-16-01 - WS-16-15


Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016


Publisher Copyright:
Copyright © 2016, Association for the Advancement of Artificial Intelligence (


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