A5014542960- Advanced Text Analysis Techniques
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Web Data Mining and Analysis
- Text and Document Classification Technologies
- Data Quality and Management
- Anomaly Detection Techniques and Applications
- Advanced Neural Network Applications
- Machine Learning in Healthcare
- Advanced Graph Neural Networks
- Artificial Intelligence in Healthcare
Zhejiang University of Science and Technology
2022-2024
Agricultural Information Institute
2023-2024
Chinese Academy of Agricultural Sciences
2023-2024
Aspect-based sentiment analysis is a fine-grained task that consists of two types subtasks: aspect term extraction and classification. In the task, current methods suffer from lack information in difficulty identifying boundaries. classification classifier cannot adapt itself to text determine local context. To address these challenges, this work proposes an adaptive semantic relative distance approach based on dependent syntactic analysis, which uses appropriate context for each increase...
Aspect-based sentiment analysis (ABSA), which aims to extract aspects and corresponding opinions from sentences determine aspect polarity, has been widely studied in recent years. Most approaches focus on the subtasks of ABSA deal with them pipeline method or end-to-end method. However, these ignore semantic information labels correlation between labels. In this study, we process various tasks a unified generative framework use instruction prompts guide model learn relationships different...
With the explosive growth in short texts on Web and an increasing number of corpora consisting texts, are playing important role various applications. Entity linking is a crucial task knowledge graphs key technology field that affects accuracy many downstream tasks natural language processing. However, compared to long entity-linking Chinese text challenging problem due serious colloquialism insufficient contexts. Moreover, existing methods for entity underutilize semantic information ignore...
Relational triple extraction is a critical step in knowledge graph construction. Compared to pipeline-based extraction, joint gaining more attention because it can better utilize entity and relation information without causing error propagation issues. Yet, the challenge with lies handling overlapping triples. Existing approaches adopt sequential steps or multiple modules, which often accumulate errors interfere redundant data. In this study, we propose an innovative model cross-attention...
Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As key NLP, relation triplet extraction is closely related to the performance of monitoring. In this paper, novel model proposed for joint entities and relations, combining conditional layer normalization with talking-head attention mechanism strengthen interaction between entity recognition extraction. addition, utilizes position information enhance...
Medication recommendation based on electronic health records (EHRs) is a significant research direction in the biomedical field, which aims to provide reasonable prescription for patients according their historical and current conditions. However, existing recommended methods have many limitations dealing with structural temporal characteristics of EHRs. These either only consider state while ignoring situation, or fail adequately assess correlations among various medical events. factors...