Name
Abstract | ||
|---|---|---|
In this paper, we have studied a series of deep convolution recurrent neural network models for automatic recognition of humor from Hindi-English code mixed text. The proposed model takes into consideration both word level and sentence level embeddings. We have observed that neural network architectures that make use of the bidirectional LSTM and CNN models along with the combined word and sentence embeddings perform better than the other baseline models.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3368567.3368576 | Proceedings of the 11th Forum for Information Retrieval Evaluation |
Keywords | Field | DocType |
Code-mixing, Convolution recurrent neural network, Humor Detection | Convolution,Computer science,Recurrent neural network,Artificial intelligence,Natural language processing,Artificial neural network,Sentence,Code-mixing | Conference |
ISBN | Citations | PageRank |
978-1-4503-7750-8 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vaibhav Shukla | 1 | 0 | 0.34 |
| Manjira Sinha | 2 | 22 | 12.94 |
| Tirthankar Dasgupta | 3 | 76 | 26.41 |