SEPT: Improving Scientific Named Entity Recognition with Span Representation
Table 1: Overall performance of scientific named entity recognition task. We report micro F1 score following the convention of NER task. All scores are taken from the test set with the corresponding highest development score.
['Models', 'Precision', 'Recall', 'F1']
We run each system on the SCIERC dataset with the same split scheme as the previous work. In BiLSTM model, we use Glove pennington2014glove, ELMo Peters:2018 and SciBERT(fine-tuned) Beltagy2019SciBERT as word embeddings and then concatenate a CRF layer at the end. In SCIIE luan2018multi, we report single task scores and use ELMo embeddings as the same as they described in their paper. To eliminate the effect of pre-trained embeddings and perform a fair competition, we add a SciBERT layer in SCIIE and fine-tune model parameters like other BERT-based models.