Titlebook: Computational Linguistics and Intelligent Text Processing; 18th International C Alexander Gelbukh Conference proceedings 2018 Springer Natu

施加

嚴(yán)厲譴責(zé)

滲入

https://doi.org/10.1007/978-3-662-10107-0t modalities, and generalizability. The framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.

施魔法

Herbert Oertel,Martin B?hle,Ulrich Dohrmanngmented 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.

GRE

Herbert Oertel,Martin B?hle,Ulrich Dohrmannre provided as a benchmark for future studies and comparisons with other emotion detection models. The best results over a set of eight emotions were obtained using a complement Na?ve Bayes algorithm with an overall accuracy of 68.12%.

易改變

Leveraging Target-Oriented Information for Stance Classificationgmented 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.

Autobiography

移植

https://doi.org/10.1007/978-3-319-77116-8artificial intelligence; emotion recognition; internet; learning algorithms; machine translations; natura

事情

978-3-319-77115-1Springer Nature Switzerland AG 2018

健談的人

Computational Linguistics and Intelligent Text Processing978-3-319-77116-8Series ISSN 0302-9743 Series E-ISSN 1611-3349