Ace 2.0: A comprehensive tool for automatic extraction, analysis, and digital profiling of the researchers in scientific communities
In the current digital era, it is remarkably convenient for researchers to share and collaborate
on novel scientific ideas. Scientists aim to accomplish these endeavors through closely …
on novel scientific ideas. Scientists aim to accomplish these endeavors through closely …
Comparing free reference extraction pipelines
T Backes, A Iurshina, MA Shahid, P Mayr - International Journal on Digital …, 2024 - Springer
In this paper, we compare the performance of several popular pre-trained reference
extraction and segmentation toolkits combined in different pipeline configurations on three …
extraction and segmentation toolkits combined in different pipeline configurations on three …
A hybrid approach and unified framework for bibliographic reference extraction
Publications are an integral part of a scientific community. Bibliographic reference extraction
from scientific publication is a challenging task due to diversity in referencing styles and …
from scientific publication is a challenging task due to diversity in referencing styles and …
Synthetic vs. real reference strings for citation parsing, and the importance of re-training and out-of-sample data for meaningful evaluations: experiments with grobid …
M Grennan, J Beel - arXiv preprint arXiv:2004.10410, 2020 - arxiv.org
Citation parsing, particularly with deep neural networks, suffers from a lack of training data
as available datasets typically contain only a few thousand training instances. Manually …
as available datasets typically contain only a few thousand training instances. Manually …
Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study
In the realm of digital libraries, efficiently managing and accessing scientific publications
necessitates automated bibliographic reference segmentation. This study addresses the …
necessitates automated bibliographic reference segmentation. This study addresses the …
Benchmarking object detection networks for image based reference detection in document images
In this paper we study the performance evaluation of state-of-the-art object detection models
for the task of bibliographic reference detection from document images. The motivation of …
for the task of bibliographic reference detection from document images. The motivation of …
Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study
RC Hidalgo, RP Elías, JM Torres-Moreno… - Data, 2024 - search.proquest.com
In the realm of digital libraries, efficiently managing and accessing scientific publications
necessitates automated bibliographic reference segmentation. This study addresses the …
necessitates automated bibliographic reference segmentation. This study addresses the …
Siamese Meta-Learning and Algorithm Selection with'Algorithm-Performance Personas'[Proposal]
Automated per-instance algorithm selection often outperforms single learners. Key to
algorithm selection via meta-learning is often the (meta) features, which sometimes though …
algorithm selection via meta-learning is often the (meta) features, which sometimes though …
[PDF][PDF] Federated meta-learning: Democratizing algorithm selection across disciplines and software libraries
J Beel - Science (AICS), 2018 - researchgate.net
There is an ever-growing number of tools for automating the machine learning pipeline, both
commercial and open source. Auto-sklearn [11, 15], Auto Weka [14], ML-Plan [18], and H2O …
commercial and open source. Auto-sklearn [11, 15], Auto Weka [14], ML-Plan [18], and H2O …