Before revision:  In this paper we extend that work, by (1) improving the quality of the training data and (2) applying BERT-based models to the problem. We use error analysis throughout our work to find problems in the dataset, improve our models and reason about the types of errors different modelsare susceptible to. We first discuss the work by Verberne et al. and other related work in Section2. We describe the improvements we make in the dataset, and the new models proposed for this task.
After revision:  In this paper we extend that work, by (1) improving the quality of the training data and (2) applying BERT-based models on the labelled data (BERT, bioBERT, sciBERT). We find that the improved training data leads to a large improvement in the quality of the trained models. In addition, the BERT models beat CRF and Flair, with recall scores around 97\% obtained with cross validation. With the best model we label a large collection of 33 thousand patents, extract the citations, and match them to publications in the Web of Science database. We extract 50\% more references than with the new models proposed for this task.