Chinese Relation extraction by Multiple instance learning

Yu Ju Chen, Jane Yung Jen Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


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.

Original languageEnglish
Title of host publicationWS-16-01
Subtitle of host publicationArtificial 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
PublisherAI Access Foundation
Number of pages5
ISBN (Electronic)9781577357599
StatePublished - 2016
Externally publishedYes
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: 12 02 201613 02 2016

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-16-01 - WS-16-15


Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States

Bibliographical note

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


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