Papers with containment relation extraction
Word-Level Loss Extensions for Neural Temporal Relation Classification (C18-1)
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| Challenge: | Unsupervised pre-trained word embeddings are used for many tasks in natural language processing to leverage unlabeled textual data. |
| Approach: | They extend the model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model to ensure that the learned word representations contain both task-specific features and more general features. |
| Outcome: | The proposed model improves on the task of extracting narrative containment relations from clinical records using a general-domain part-of-speech tagger as linguistic resource. |