Papers by Swayambhu Nath Ray
AD3: Attentive Deep Document Dater (D18-1)
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| Challenge: | Existing methods to predict creation time of documents are based on time-stamp metadata, but none are available. |
| Approach: | They propose an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner. |
| Outcome: | The proposed system outperforms neural and non-neural baselines on multiple real-world datasets. |
Dating Documents using Graph Convolution Networks (P18-1)
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| Challenge: | Existing approaches for document dating assume accurate knowledge of document date, but this is not always available for arbitrary documents from the Web. |
| Approach: | They propose a Graph Convolutional Network (GCN) based document dating approach which exploits syntactic and temporal graph structures of document in a principled way. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets by 19% absolute accuracy points. |
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing KG embedding methods ignore this temporal dimension while learning embedds of the KG elements. |
| Approach: | They propose a temporally aware KG embedding method which incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. |
| Outcome: | The proposed method performs KG inference using temporal guidance and predicts scopes for relational facts with missing time annotations. |