中央研究院 資訊科學研究所

Due to technological advancement, the amount of data generated in recent years has increased drastically, fueling the need and interest in artificial intelligence and big data analysis. Among the many types of big data, text data is the most important and challenging type of data as it contains a lot of information. Therefore, the ability to analyze unstructured text data has gained much prominence in the artificial intelligence industry, as seen in many recent AI applications, such as intelligent robots, voice assistant, and self-driving car. In this speech, I will give an introduction on how to apply natural language processing and text mining technology to analyze social media and clinical text data. Through this case sharing, you can have a better understanding of how technology can be applied in real life scenarios.

Yung-Chun Chang received his Ph.D. degree in information management from National Taiwan University, Taipei, Taiwan, in 2016. Currently, he is an assistant professor of the Graduate Institute of Data Science, Taipei Medical University, Taiwan. In 2008, he served the Research and Development Alternative Service (研發替代役) as a software engineer in Academia Sinica. Over 3 years development experiences of GOING (自然輸入法) (Natural Input Method, links), which is one of the most famous intelligent Chinese input method. From 2016 to 2017, he was a postdoctoral research fellow of the Institute of Information Science, Academia Sinica. He received Academia Sinica Postdoctoral Research Fellow Award in 2016 (links). From 2010, he worked as a research assistant in the Intelligent Agent System Lab (IASL) at Institute of Information Science, Academia Sinica. During last three years, he have successfully completed several research tasks. His papers have appeared in IEEE Transactions on Knowledge and Data Engineering, ACL, Database, IEEE System Journal, Computational Intelligence, JBI, PACIS, AIRS, etc. His research interests include natural language, knowledge discovery from text, information extraction, relation extraction, and question answering. His recent works include (but not limit to): topic-oriented social relationship extraction, principle-based approach for NLP applications, text mining for sentiment analysis, question answering system, and intelligent Chatbot of deep language understanding.