# Named entity recognition in chemical patents using ensemble of contextual language models

arXiv: https://arxiv.org/abs/2007.12569 · HTML: https://mlsanity.com/paper/2007.12569/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Can BERT Dig It? -- Named Entity Recognition for Information Retrieval in the Archaeology Domain](https://mlsanity.com/paper/2106.07742/) (2021)
- [Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events](https://mlsanity.com/paper/2302.04185/) (2023)
- [Merged ChemProt-DrugProt for Relation Extraction from Biomedical Literature](https://mlsanity.com/paper/2405.18605/) (2024)

## Abstract (arXiv)

Chemical patent documents describe a broad range of applications holding key reaction and compound information, such as chemical structure, reaction formulas, and molecular properties. These informational entities should be first identified in text passages to be utilized in downstream tasks. Text mining provides means to extract relevant information from chemical patents through information extraction techniques. As part of the Information Extraction task of the Cheminformatics Elsevier Melbourne University challenge, in this work we study the effectiveness of contextualized language models to extract reaction information in chemical patents. We assess transformer architectures trained on a generic and specialised corpora to propose a new ensemble model. Our best model, based on a majority ensemble approach, achieves an exact F1-score of 92.30% and a relaxed F1-score of 96.24%. The results show that ensemble of contextualized language models can provide an effective method to extract information from chemical patents.