Tsung-Hsien Yang


pdf bib
SCU-MESCLab at ROCLING-2023 Shared Task:Named Entity Recognition Using Multiple Classifier Model
Tzu-En Su | Ruei-Cyuan Su | Ming-Hsiang Su | Tsung-Hsien Yang
Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023)


pdf bib
SCU-MESCLab at ROCLING-2022 Shared Task: Named Entity Recognition Using BERT Classifier
Tsung-Hsien Yang | Ruei-Cyuan Su | Tzu-En Su | Sing-Seong Chong | Ming-Hsiang Su
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)

In this study, named entity recognition is constructed and applied in the medical domain. Data is labeled in BIO format. For example, “muscle” would be labeled “B-BODY” and “I-BODY”, and “cough” would be “B-SYMP” and “I-SYMP”. All words outside the category are marked with “O”. The Chinese HealthNER Corpus contains 30,692 sentences, of which 2531 sentences are divided into the validation set (dev) for this evaluation, and the conference finally provides another 3204 sentences for the test set (test). We use BLSTM_CRF, Roberta+BLSTM_CRF and BERT Classifier to submit three prediction results respectively. Finally, the BERT Classifier system submitted as RUN3 achieved the best prediction performance, with an accuracy of 80.18%, a recall rate of 78.3%, and an F1-score of 79.23.


pdf bib
Speech Emotion Recognition Based on CNN+LSTM Model
Wei Mou | Pei-Hsuan Shen | Chu-Yun Chu | Yu-Cheng Chiu | Tsung-Hsien Yang | Ming-Hsiang Su
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

Due to the popularity of intelligent dialogue assistant services, speech emotion recognition has become more and more important. In the communication between humans and machines, emotion recognition and emotion analysis can enhance the interaction between machines and humans. This study uses the CNN+LSTM model to implement speech emotion recognition (SER) processing and prediction. From the experimental results, it is known that using the CNN+LSTM model achieves better performance than using the traditional NN model.


pdf bib
預訓練詞向量模型應用於客服對話系統意圖偵測之研究(Study on Pre-trained Word Vector Model Applied to Intent Detection of Customer Service Dialogue System)
Guan-Yu Chen | Min-Feng Kuo | Tsung-Hsien Yang | Chun-Hsun Chen | I-Bin Liao
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)