Lab Director: Jie Wang
Location: Dandeneau Hall 408
Jie Wang's personal website
The group performs research in text mining algorithms and systems, document engineering, and data modeling and its applications. Some of the projects that we have worked on include intelligent text automation, document summary, domain extraction and statistical measurement in knowledge networks, and news classification.
The Text Mining and Document Engineering Group hires Ph.D. students and occasionally master’s students. However, they do plan on hiring undergraduates in the future.
Linear reading from the beginning to the end is the normal way of reading for the purpose of learning new knowledge. Is there a better way to read for understanding? Let’s think outside the box and turn linear reading to hierarchical reading. Imagine that we have access to an oracle that ranks the sentences of the given text according to their importance, allowing us to read blocks of sentences one at a time in descending order of importance, focus on the most important block of sentences, and read subsequent blocks to strengthen understandings of earlier blocks. Moreover, the oracle also generates questions and evaluates answers. Dooyeed is an AI-assisted text mining system to facilitate reading for understanding to meet the AEE requirements: Accurate and Efficient for the oracle and Effective for the reader. Dooyeed outperforms each individual human judge over the SummBank benchmarks and compares favorably with the combined rankings of all judges. Dooyeed currently supports two major languages: English and Chinese. This invention is made by Jie Wang and assisted by his Ph.D. students, Students who have participated in the research and development of Dooyeed include Cheng Zhang, Hao Zhang, Changfeng Yu, Yicheng Sun, and You Zhou. Eola Solutions, Inc. provided financial support.
The Text Mining group has graduated a number of Ph.D. students and MS students, including Yiqi Bai (now at Facebook Seattle), Ming Jia (Facebook Seattle), Liqun Shao (Microsoft Cambridge), Jingwen Wang (Elizabethtown College), and Wenjing Yang (Dell EMC).