Name
Abstract | ||
|---|---|---|
Most of the previous emotion classifications are based on binary or ternary classifications, and the final emotion classification results contain only one type of emotion. There is little research on multi-emotional coexistence, which has certain limitations on the restoration of human's true emotions. Aiming at these deficiencies, this paper proposes a Bidirectional Long-Short Term Memory Multiple Classifiers (BLSTM-MC) model to study the five classification problems in code-switching text, and obtains text contextual relations through BLSTM-MC model. It fully considers the relationship between different emotions in a single post, at the same time, the Attention mechanism is introduced to find the importance of different features and predict all emotions expressed by each post. The model achieved third place in all submissions in the conference NLP&&CC_task1 2018. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-99501-4_16 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Multiple emotion classification,Code-switching texts,Attention mechanism,BLSTM multiple classifiers | Code-switching,Computer science,Emotion classification,Natural language processing,Artificial intelligence,Binary number | Conference |
Volume | ISSN | Citations |
11109 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tingwei Wang | 1 | 0 | 0.34 |
| Xiaohua Yang | 2 | 0 | 0.68 |
| Chunping Ouyang | 3 | 6 | 3.35 |
| Aodong Guo | 4 | 0 | 0.34 |
| Yongbin Liu | 5 | 58 | 11.05 |
| Zhixing Li | 6 | 2 | 1.04 |