Kang Zhou: Enhancing Efficiency and Training Data Acquisition

February 16, 2024

In today's digital age, the Internet stands as an invaluable resource capable of extracting a wealth of information. However, there is a lot of information that can make it challenging to navigate.

Luckily, there is a solution. Through the process of information extraction, we can navigate through vast volumes of diverse data to extract the specific information required for informed decision-making and drawing conclusions. However, the significant cost associated with large-scale information extraction systems is proving to continue to be a challenge for researchers.

Recognizing this challenge, researchers such as Kang Zhou (photographed attending the 37th AAAI Conference on Artificial Intelligence), a Ph.D. student in Computer Science at Iowa State University, are working on tackling this issue and enhancing the efficiency of information extraction systems. Zhou works with Qi Li, an Assistant Professor of Computer Science at Iowa State University. His research is supported by her grant from the United States Department of Agriculture, “QTLdb and CorrDB: Resources to Help Close the Genotype to Phenotype Gap.” James Reecy, a Professor and Associate Vice President for Research in the Department of Animal Science at Iowa State University, is the primary investigator of the grant. Zhou’s recent paper, “Improving Distantly Supervised Relation Extraction by Natural Language Inference,” was accepted to the 37th AAAI Conference on Artificial Intelligence, one of the most prestigious and influential conferences in the field of artificial intelligence, where researchers, practitioners, and industry professionals gather to present and discuss the latest advancements in AI.

Zhou’s research focuses on the development of effective and efficient approaches to train high-quality information extraction models with little human effort tailored for low-resourced domains. Through his research, he developed techniques of making annotations for extraction tasks more efficient and lower cost by acquiring training data from various information sources, ensuring it aligns with the specific requirements of information extraction.

Overall, Zhou’s work aims to pave the way for more efficient and effective extraction processes, going beyond the limitations imposed by the current cost-intensive nature of large-scale information extraction systems. Researchers and practitioners alike would be able to extract valuable insights from the vast sea of digital information, unlocking its full potential.

News Type: