# ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition

arXiv: https://arxiv.org/abs/1911.10436 · HTML: https://mlsanity.com/paper/1911.10436/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [The Diminishing Returns of Masked Language Models to Science](https://mlsanity.com/paper/2205.11342/) (2022)
- [Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch](https://mlsanity.com/paper/2403.14729/) (2024)
- [In Situ Answer Sentence Selection at Web-scale](https://mlsanity.com/paper/2201.05984/) (2022)
- [Extracting Space Situational Awareness Events from News Text](https://mlsanity.com/paper/2201.05721/) (2022)
- [Towards understanding evolution of science through language model series](https://mlsanity.com/paper/2409.09636/) (2024)

## Abstract (arXiv)

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.