Auto-labelling entities in low-resource text: a geological case study

Majigsuren Enkhsaikhan, Wei Liu, Eun Jung Holden, Paul Duuring

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)695-715
Number of pages21
JournalKnowledge and Information Systems
Issue number3
Publication statusPublished - Mar 2021


Dive into the research topics of 'Auto-labelling entities in low-resource text: a geological case study'. Together they form a unique fingerprint.

Cite this