APRCOIE: An open information extraction system for Chinese
Yan Liao,
Jialin Hua,
Liangqing Luo,
Weiying Ping,
Xuewen Lu,
Yuansheng Zhong
Affiliations
Yan Liao
Key Laboratory of Data Science in Finance and Economics, and School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China
Jialin Hua
Key Laboratory of Data Science in Finance and Economics, and School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China; Corresponding author.
Liangqing Luo
Key Laboratory of Data Science in Finance and Economics, and School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China
Weiying Ping
Key Laboratory of Data Science in Finance and Economics, and School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China
Xuewen Lu
Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
Yuansheng Zhong
Key Laboratory of Data Science in Finance and Economics, and School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, China
Open information extraction (OIE) is critical in natural language processing. Compared to the flourishing development of OIE systems in English, very few high-quality Chinese OIE systems are publicly available. APRCOIE is a system that conducts open information extraction for Chinese text data. Differing from traditional rule-based approaches and learning-based methods, the new system innovatively employs automated methods to explore the nature of complex Chinese literal characteristics, thus generating a large number of extraction rules and subsequently establishing the extraction model. The feature of low resource demand makes APRCOIE easy to deploy in various real applications.
