This paper describes our system for the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA). We participate in Track A (DimABSA) and Track B (DimStance), both of which involve Subtask 1 – predicting continuous valence–arousal (VA) scores for given text–aspect pairs in English and Chinese.Our system combines pre-trained multilingual transformers with aspect-marker input encoding and dual regression heads for VA prediction, trained with a 5-fold cross-validation ensemble. We select XLM-RoBERTa-large as the backbone for Track A and mDeBERTa-v3-base for Track B based on systematic model comparison on the development sets. On the official test sets, our system substantially outperforms the organizer-provided baselines across all language domain settings. On the unofficial postevaluation leaderboard, the system achieves strong results on Chinese subsets, ranking 1st on zho-env (Track B) and 2nd on zho-fin (Track A).

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.

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.