ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
This work addresses the need for densely-labeled semantic classification in the science exam domain, which is incremental as it applies existing methods to a new, high-density dataset.
The authors tackled the problem of sparse entity labels in named entity recognition by introducing ScienceExamCER, a densely-labeled corpus with 133k mentions in the science exam domain, where 96% of content words are annotated with fine-grained semantic classes. They showed that a modified BERT-based model achieved an F1 score of 0.85 on this task, indicating strong utility for downstream science question answering.
Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled semantic classification corpus of 133k mentions in the science exam domain where nearly all (96%) of content words have been annotated with one or more fine-grained semantic class labels including taxonomic groups, meronym groups, verb/action groups, properties and values, and synonyms. Semantic class labels are drawn from a manually-constructed fine-grained typology of 601 classes generated through a data-driven analysis of 4,239 science exam questions. We show an off-the-shelf BERT-based named entity recognition model modified for multi-label classification achieves an accuracy of 0.85 F1 on this task, suggesting strong utility for downstream tasks in science domain question answering requiring densely-labeled semantic classification.