Papers by John Culnan
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition identifies common classes of noun phrases in text, but these entity labels are sparse, limiting utility to downstream tasks. |
| Approach: | They propose a name-based named entity recognition model that annotates all content words with a fine-grained semantic class label. |
| Outcome: | The proposed model achieves 0.85 F1 on the science exam domain domain . the proposed model is a powerful tool for question answering and inference . |