Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models (P19-1)
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| Challenge: | Recent studies show that document classifiers can become more stable over time when trained in ways that account for temporal variations. |
| Approach: | They propose a method for embedding diachronic word embedds into document classification models . they propose 'time-driven neural classification model' that accounts for temporal variations . |
| Outcome: | The proposed model can be trained on six corpora and make it more robust over time. |
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