Papers by Guanying Wang
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (N19-1)
Copied to clipboard
| Challenge: | Existing distance supervised relation extraction models for long-tail data are inadequate for many applications. |
| Approach: | They propose to leverage implicit relational knowledge among class labels and learn explicit relational knowing using graph convolution networks. |
| Outcome: | The proposed approach outperforms baselines for long-tail relations on a large-scale dataset. |
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding (D18-1)
Copied to clipboard
| Challenge: | Existing methods to generate large scale labeled data for relation extraction produce noisy relation labels when there are multiple relationships between entities. |
| Approach: | They propose a method which assumes that a pair of entities appears in a Knowledge Graph and trains a relation classifier. |
| Outcome: | The proposed method performs well in the current distant supervision dataset. |