Ziyu Yang


2022

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BERT 4EVER@LT-EDI-ACL2022-Detecting signs of Depression from Social Media:Detecting Depression in Social Media using Prompt-Learning and Word-Emotion Cluster
Xiaotian Lin | Yingwen Fu | Ziyu Yang | Nankai Lin | Shengyi Jiang
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

In this paper, we report the solution of the team BERT 4EVER for the LT-EDI-2022 shared task2: Homophobia/Transphobia Detection in social media comments in ACL 2022, which aims to classify Youtube comments into one of the following categories: no,moderate, or severe depression. We model the problem as a text classification task and a text generation task and respectively propose two different models for the tasks.To combine the knowledge learned from these two different models, we softly fuse the predicted probabilities of the models above and then select the label with the highest probability as the final output.In addition, multiple augmentation strategies are leveraged to improve the model generalization capability, such as back translation and adversarial training.Experimental results demonstrate the effectiveness of the proposed models and two augmented strategies.

2021

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A Visualization Approach for Rapid Labeling of Clinical Notes for Smoking Status Extraction
Saman Enayati | Ziyu Yang | Benjamin Lu | Slobodan Vucetic
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

Labeling is typically the most human-intensive step during the development of supervised learning models. In this paper, we propose a simple and easy-to-implement visualization approach that reduces cognitive load and increases the speed of text labeling. The approach is fine-tuned for task of extraction of patient smoking status from clinical notes. The proposed approach consists of the ordering of sentences that mention smoking, centering them at smoking tokens, and annotating to enhance informative parts of the text. Our experiments on clinical notes from the MIMIC-III clinical database demonstrate that our visualization approach enables human annotators to label sentences up to 3 times faster than with a baseline approach.