Computer Science Department Thesis Defense - Jingtian Zhao

Event Date: 
Friday, May 12, 2023 - 4:00pm to 5:30pm EDT
Event Location: 
Event Contact Name: 
Rachael Wang
Event Contact E-mail: 

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Jingtian Zhao

Thesis title: Adding Time-series Data to Enhance Performance of Natural Language Processing Tasks

Abstract: In the past few decades, with the explosion of information, a large number of computer scientists have devoted themselves to analyzing collected data and applying these findings to many disciplines. Natural language processing (NLP) has been one of the most popular areas for data analysis and pattern recognition. A significantly large amount of data is obtained in text format due to the ease of access nowadays. Most modern techniques focus on exploring large sets of textual data to build forecasting models; they tend to ignore the importance of temporal information which is often the main ingredient to determine the performance of analysis, especially in the public policy view. The contribution of this paper is three-fold. First, a dataset called COVID-News is collected from three news agencies, which consists of article segments related to wearing masks during the COVID-19 pandemic. Second, we propose a long-short term memory (LSTM)-based learning model to predict the attitude of the articles from the three news agencies towards wearing a mask with both temporal and textural information. Then we added the BERT model to further improve and enhance the performance of the proposed model. Experimental results on the COVID-News dataset show the effectiveness of the proposed LSTM-based algorithm.

Committee Members:
Dr. Yimin Yang (supervisor, committee chair), Dr. Ruizhong Wei (co-supervisor), Dr. Amin Safaei, Dr. Thangarajah Akilan (Software Engineering)

Please contact for the Zoom link.
Everyone is welcome.