訊息公告

【SEMINAR】109.06.17網路工程研究所論文研討演講資訊

Topic: Choose No More: Combining Keyword Search and Supervised Learning

 

Speaker: Mr.Eugene Yang

 

Date: 6/17 (Wed.) 3:30-5:00PM

 

Location: Google Hangouts Meet

                  URL is https://meet.google.com/jmz-oxqu-euw

 

Bio
Eugene Yang is a computer science Ph.D. candidate at Georgetown University under the advice of Ophir Frieder, Jeremy Fineman, and David D. Lewis. He received a bachelor's degree in quantitative finance from National Tsing Hua University. His research focuses on total recall retrieval and technology-assisted review, especially in the legal applications. His interest includes Bayesian supervised learning, sequential decision problems, and the explainability of machine learning models.

 

Abstract:

Traditionally, supervised classification and information retrieval are considered as distinct problems with differing input. While classification requires a set of annotated data points, retrieval models only demand a textual query to rank the documents. Classification models, in contrast, once trained, sustain greater accuracy and efficiency at separating the wheat from the chaff. The obvious question is: Given both forms of information — textual keywords and labeled documents -- can we utilize both? Ignoring either is information loss; combining them is believed complicated. In this talk, within the domain of legal information processing, we develop an integration framework that combines both information types into a single model. The resulting approach capitalizes on the advantages of each information type, achieving a resource-efficient and accurate system. Ethical issues of machine learning within legal applications are likewise addressed.

earning models.