GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity
  Extraction Focused on Machine Learning Models and Datasets
Wolfgang Otto Matthäus Zloch Lu Gan Saurav Karmakar Stefan Dietze
Named Entity Recognition (NER) models play a crucial role in various NLP
tasks, including information extraction (IE) and text understanding. In
academic writing, references to machine learning models and datasets are
fundamental components of various computer science publications and necessitate
accurate models for identification. Despite the advancements in NER, existing
ground truth datasets do not treat fine-grained types like ML model and model
architecture as separate entity types, and consequently, baseline models cannot
recognize them as such. In this paper, we release a corpus of 100 manually
annotated full-text scientific publications and a first baseline model for 10
entity types centered around ML models and datasets. In order to provide a
nuanced understanding of how ML models and datasets are mentioned and utilized,
our dataset also contains annotations for informal mentions like "our
BERT-based model" or "an image CNN". You can find the ground truth dataset and
code to replicate model training at https://data.gesis.org/gsap/gsap-ner.