Enhancing the application value of data through discourse analysis, lifelog mining, and numeral understanding

Appier, an artificial intelligence software-as-a-service (SaaS) company announced today the 2 ^nd Appier AI Chair Professor award recipient, Professor Hsin-Hsi Chen of Department of Computer Science and Information Engineering, National Taiwan University. With expertise in the AI applications of natural language processing, information retrieval and extraction, and web mining, Professor Chen has been focused on the research topics of discourse analysis, lifelog mining, and numeral understanding in recent years. Professor Chen integrates data and knowledge to deal with various levels of representations, and conducts Chinese discourse analysis models systematically.

Professor Chen’s research ranges from discourse analysis to content understanding. Through lifelog mining, personal big data can be collected and used to create personal knowledge graphs, over which a question answering system helps people recall information. Chen also carried out a series of studies on numeral understanding to analyze data credibility and determine the rationale and persuasiveness of information. This study can be applied to the analysis of expert opinions in different domains, including stock analysis, poll analysis, and those in other information sectors that rely on numeral understanding to perform inductive reasoning and make decisions.

Professor Chen said, “Appier leverages AI technologies to help businesses overcome challenges in digital marketing and has opened up a new path for AI applications with unlimited potential and immense market value. By incorporating digital marketing with image recognition, natural language understanding, and automated machine learning, we can build on the existing foundation of digital marketing and achieve a greater breakthrough. I am honored to have been selected as this year’s Appier AI Chair Professor. With Appier’s support for cutting-edge research, I believe that combining text and numerical analyses will help people gain a better understanding of domain knowledge, link industry and academia trends, and inject diverse innovative thinking and influence into the industry and academia.” Professor Chen has also expanded his scope of research to the field of healthcare. He founded the Center of Intelligent Healthcare at National Taiwan University Hospital to integrate AI into healthcare and coordinate interdisciplinary collaborations between healthcare professionals and AI experts. By citing the expertise of physicians, the complementarity between data and professional knowledge can be improved, and physician-patient interview can be incorporated into the healthcare knowledge base to enhance the interaction between physician and patient. This study primarily uses natural language processing and discourse analysis technologies to boost the added value of medical data, thereby creating a more robust healthcare knowledge base.

During his tenure as the Director of the MOST-Sponsored Joint Research Center for AI Technology and All Vista Healthcare (AINTU), Professor Chen coordinated interdisciplinary and interdepartmental studies on machine learning, deep learning, and data mining. He believes that in order to take the AI development in Taiwan to the next level, it’s necessary to set clear project goals and platforms, and provide resources to attract top international talent to dedicate their career to this cause, thus ensuring greater influence and competitiveness. Another key to boosting international visibility is to continue to publish papers in top international conferences in order to gain a voice on the international stage. Professor Chen has long played an important role in major international conferences on information retrieval and natural language processing. Through the efforts of Chen and his team, AINTU helped Taiwan win the rights to host ACM SIGIR Conference in 2023. As the top academic conference in information retrieval, the conference will provide an opportunity for the world to see Taiwan’s research capabilities.

Regarding the future bottlenecks for AI applications, Professor Chen pointed out that the attribution of legal rights and liabilities, and the ethical issue of data privacy will be the two main points of concern. AI in law is a brand-new field requiring further discussion on subsequent related issues. For example, the determination of liability in a car accident involving autonomous vehicles and underwriting of auto insurance, the road traffic safety laws and vehicle safety inspection criteria for autonomous vehicles all require further discussion among experts before formulating the relevant laws. The ethics of data privacy is another important issue for AI applications. For example, we need to determine how the use of financial or medical data can be regulated so as to allow for progress in the overall industries while ensuring privacy protection. In addition, whether AI models are explainable and credible, and whether there are biases in AI models and data are AI issues and challenges that arose in recent years, waiting to be resolved and overcome.

Appier is devoted to the development of AI talent in Taiwan and believes that communication between the industry and academic sectors will facilitate the development of innovative technologies and encourage people to adopt AI technologies in business and social settings, thus accelerating the progress of AI democratization. For this reason, we established the Appier AI Chair Professor Program to reward distinguished scholars and experts who are undertaking academic education and forward-looking research in the field of AI to drive research and development of AI technologies and popularize their diverse applications.

About Appier

Appier is a software-as-a-service (SaaS) company that uses artificial intelligence to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange. Visit www.appier.com for more information.