Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection (C18-1)
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Tirthankar Ghosal, Vignesh Edithal, Asif Ekbal, Pushpak Bhattacharyya, George Tsatsaronis, Srinivasa Satya Sameer Kumar Chivukula
| Challenge: | Existing methods for document-level novelty detection are limited and do not require manual feature engineering. |
| Approach: | They propose a deep Convolutional Neural Networks based model to classify a document as novel or redundant on the basis of documents already seen by the system. |
| Outcome: | The proposed model outperforms the state-of-the-art on a document-level novelty detection dataset by a margin of 5% in terms of accuracy. |
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