Abstract
We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub.
Highlights
We study the task of recognizing named datasets in scientific articles as a Named Entity
Notice that the partial and exact match scores are closest for SciBERT
An error analysis shows that SciBERT is especially good in learning the beginning of a dataset mention
Summary
The overwhelming volume of scientific papers have made extracting knowledge from them an unmanageable task [30], making automatic IE especially relevant for this domain [31]. Research on the dataset name extraction task uses a great variety of methods throughout the NER spectrum, including, but not limited to, the following: rule-based, BiLSTM-CRF and BERT [3,6,7,8,9,10,11,12,13,14]. Computer science papers, and the remaining 82% consisting of papers from the biomedical domain This model, which was specially created for knowledge extraction in the scientific domain, achieves better performance, in comparison to BERT, in the computer science domain
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