Fine-Grained Entity Recognition
The FIGER dataset is an entity recognition dataset where entities are labelled using fine-grained system 112 tags, such as person/doctor, art/written_work and building/hotel. The tags are derivied from Freebase types. The training set consists of Wikipedia articles automatically annotated with distant supervision approach that utilizes the information encoded in anchor links. The test set was annotated manually.
Source: http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5152
Variants: FIGER
This dataset is used in 2 benchmarks:
| Task | Model | Paper | Date |
|---|---|---|---|
| Entity Typing | LITE | Ultra-fine Entity Typing with Indirect … | 2022-02-12 |
| Entity Linking | ERNIE | ERNIE: Enhanced Language Representation with … | 2019-05-17 |
Recent papers with results on this dataset: