Datasets are critical for scientific research, playing an important role in
replication, reproducibility, and efficiency. Researchers have recently shown
that datasets are becoming more important for science to function properly,
even serving as artifacts of study themselves. However, citing datasets is not
a common or standard practice in spite of recent efforts by data repositories
and funding agencies. This greatly affects our ability to track their usage and
importance. A potential solution to this problem is to automatically extract
dataset mentions from scientific articles. In this work, we propose to achieve
such extraction by using a neural network based on a Bi-LSTM-CRF architecture.
Our method achieves F1 = 0.885 in social science articles released as part of
the Rich Context Dataset. We discuss the limitations of the current datasets
and propose modifications to the model to be done in the future.

本论文基于 Bi-LSTM-CRF 架构的神经网络，提出了从科学文章中自动提取数据集提及的解决方案，并在社会科学文章中的 Rich Context 数据集上实现了 F1 = 0.885 的效果，同时讨论了当前数据集的局限性和未来模型的改进方向。

科学文章中使用 Bi-LSTM-CRF 模型进行数据集提取

Dataset Mention Extraction in Scientific Articles Using Bi-LSTM-CRF  Model

We present an analysis of the performance of Federated Learning in a
paradigmatic natural-language processing task: Named-Entity Recognition (NER).
For our evaluation, we use the language-independent CoNLL-2003 dataset as our
benchmark dataset and a Bi-LSTM-CRF model as our benchmark NER model. We show
that federated training reaches almost the same performance as the centralized
model, though with some performance degradation as the learning environments
become more heterogeneous. We also show the convergence rate of federated
models for NER. Finally, we discuss existing challenges of Federated Learning
for NLP applications that can foster future research directions.

本文通过将联邦学习应用在命名实体识别任务中，使用具有语言无关性的 CoNLL-2003 数据集作为基准数据集，Bi-LSTM-CRF 模型作为基准模型，研究了联邦学习的性能，展示了联邦学习相对于集中式模型在不同的异构学习环境下的性能降低，并讨论了联邦学习在自然语言处理应用领域中的现存挑战和未来研究方向。