Papers with general purpose extractors
Matching the Blanks: Distributional Similarity for Relation Learning (P19-1)
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| Challenge: | Efforts to build general purpose relation extractors that can model arbitrary relations are limited in their ability to generalize. |
| Approach: | They propose to build task-agnostic relation representations solely from entity-linked text to extend Harris’ distributional hypothesis to relations. |
| Outcome: | The proposed representations outperform previous methods on SemEval 2010 Task 8, KBP37, and TACRED even without using any of the task’s training data. |