Name
Abstract | ||
|---|---|---|
Studies on named entity recognition (NER) often require a substantial amount of human-annotated training data. This makes technical domain-specific NER from industry data especially challenging as labelled data are scarce. Despite English as the surface language, technical jargon and writing conventions used in technical documents render the low-resource language challenges where techniques such as transfer learning hardly work. Relieving labour intensive annotations using automatic labelling is thus an important research topic, seeking ways to obtain labelled data quickly and consistently. In this work, we propose an iterative deep learning NER framework using distant supervision for automatic labelling of domain-specific datasets. The framework is applied to mineral exploration reports and produced a large BIO-annotated dataset with six geological categories. This quality-labelled dataset, OzROCK, is made publicly available to support future research on technical domain NER. Experimental results demonstrated the effectiveness of this approach, further confirmed by domain experts. The generalisation ability is verified by applying the framework to two other datasets: one for disease names and the other for chemical names. Overall, our approach can effectively reduce annotation efforts by identifying a much smaller subset, that is challenging for automatic labelling thus requires attention from human experts. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10115-020-01532-6 | KNOWLEDGE AND INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Geological named entity recognition, Geological information extraction, Sequence labelling | Journal | 63 |
Issue | ISSN | Citations |
3 | 0219-1377 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Majigsuren Enkhsaikhan | 1 | 15 | 2.92 |
| Wei Liu | 2 | 14 | 4.57 |
| Eun-Jung Holden | 3 | 105 | 13.82 |
| P. Duuring | 4 | 0 | 1.01 |