Tzu-Mi Lin


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NCUEE-NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced RoBERTa Transformers
Tzu-Mi Lin | Jung-Ying Chang | Lung-Hao Lee
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

This paper describes our proposed system design for the WASSA 2023 shared task 1. We propose a unified architecture of ensemble neural networks to integrate the original RoBERTa transformer with two sentiment-enhanced RoBERTa-Twitter and EmoBERTa models. For Track 1 at the speech-turn level, our best submission achieved an average Pearson correlation score of 0.7236, ranking fourth for empathy, emotion polarity and emotion intensity prediction. For Track 2 at the essay-level, our best submission obtained an average Pearson correlation score of 0.4178 for predicting empathy and distress scores, ranked first among all nine submissions.

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Overview of the ROCLING 2023 Shared Task for Chinese Multi-genre Named Entity Recognition in the Healthcare Domain
Lung-Hao Lee | Tzu-Mi Lin | Chao-Yi Chen
Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023)


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NCUEE-NLP@SMM4H’22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models
Tzu-Mi Lin | Chao-Yi Chen | Yu-Wen Tzeng | Lung-Hao Lee
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task

This study describes our proposed system design for the SMM4H 2022 Task 8. We fine-tune the BERT, RoBERTa, ALBERT, XLNet and ELECTRA transformers and their connecting classifiers. Each transformer model is regarded as a standalone method to detect tweets that self-reported chronic stress. The final output classification result is then combined using the majority voting ensemble mechanism. Experimental results indicate that our approach achieved a best F1-score of 0.73 over the positive class.

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NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model
Lung-Hao Lee | Chien-Huan Lu | Tzu-Mi Lin
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

This study describes the model design of the NCUEE-NLP system for the Chinese track of the SemEval-2022 MultiCoNER task. We use the BERT embedding for character representation and train the BiLSTM-CRF model to recognize complex named entities. A total of 21 teams participated in this track, with each team allowed a maximum of six submissions. Our best submission, with a macro-averaging F1-score of 0.7418, ranked the seventh position out of 21 teams.