NanoNER: Named Entity Recognition for nanobiology using experts' knowledge and distant supervision

TL;DR

NanoNER is a high-accuracy NER model for nanobiology that leverages domain experts' knowledge and distant supervision to identify known and novel entities with minimal manual effort, demonstrating strong results on a large corpus.

Contribution

The paper introduces NanoNER, a novel NER approach combining expert-annotated ontologies and distant supervision, enabling efficient recognition of entities in nanobiology texts.

Findings

F1-score of 0.98 on known entities

Achieved 77-81% precision on new entities

Discovered up to 30% of ablated terms

Abstract

Here we present the training and evaluation of NanoNER, a Named Entity Recognition (NER) model for Nanobiology. NER consists in the identification of specific entities in spans of unstructured texts and is often a primary task in Natural Language Processing (NLP) and Information Extraction. The aim of our model is to recognise entities previously identified by domain experts as constituting the essential knowledge of the domain. Relying on ontologies, which provide us with a domain vocabulary and taxonomy, we implemented an iterative process enabling experts to determine the entities relevant to the domain at hand. We then delve into the potential of distant supervision learning in NER, supporting how this method can increase the quantity of annotated data with minimal additional manpower. On our full corpus of 728 full-text nanobiology articles, containing more than 120k entity occurrences, NanoNER obtained a F1-score of 0.98 on the recognition of previously known entities. Our model also demonstrated its ability to discover new entities in the text, with precision scores ranging from 0.77 to 0.81. Ablation experiments further confirmed this and allowed us to assess the dependency of our approach on the external resources. It highlighted the dependency of the approach to the resource, while also confirming its ability to rediscover up to 30% of the ablated terms. This paper details the methodology employed, experimental design, and key findings, providing valuable insights and directions for future related researches on NER in specialized domain. Furthermore, since our approach require minimal manpower , we believe that it can be generalized to other specialized fields.