Name
Abstract | ||
|---|---|---|
Classifying the stance expressed in text towards specific target, namely stance detection, is a challenging task. The biggest distinction between stance detection and ordinary sentiment classification is that the determination of the stance is dependent on target while the target might not be explicitly mentioned in text. This indicates that the stance detection is not only dependent on the text content but also highly determined by the concerned target. To this end, we propose a neural network based model for stance detection, which leverages target-oriented information by utilizing target-augmented embedding and attention mechanism. The attention mechanism here is expected to locate the important parts of a text. The evaluation on SemEval 2016 Task 6 Twitter Stance Detection dataset shows that our proposed model achieves the state-of-the-art results. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-77116-8_3 | COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2017, PT II |
Keywords | Field | DocType |
Stance detection,Neural attention,Sentiment analysis | Embedding,SemEval,Computer science,Sentiment analysis,Natural language processing,Artificial intelligence,Artificial neural network | Conference |
Volume | ISSN | ISBN |
10762 | 0302-9743 | 978-3-319-77116-8 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Du Jiachen | 1 | 36 | 9.02 |
| Xu Ruifeng | 2 | 432 | 53.04 |
| Gui Lin | 3 | 9 | 1.54 |
| Wang Xuan | 4 | 11 | 1.11 |