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TSE-NER: An Iterative Approach for Long-Tail Entity Extraction in Scientific Publications
[chapter]
2018
Lecture Notes in Computer Science
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This paper presents an iterative approach for training NER and NET classifiers in scientific publications that relies on minimal human input, namely a small seed set of instances for the targeted entity ...
We evaluate our approach on scientific publications, focusing on the long-tail entities types Datasets, Methods in computer science publications, and Proteins in biomedical publications. ...
Conclusion We presented a novel approach for the extraction of domain-specific long-tail entities from scientific publications. ...
doi:10.1007/978-3-030-00671-6_8
fatcat:bzaybmrb6jbancylfi66okppbm
Identifying used methods and datasets in scientific publications
2021
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose an approach to identifying methods and datasets in texts that have actually been used by the authors. ...
Although it has become common to assess publications and researchers by means of their citation count (e.g., using the h-index), measuring the impact of scientific methods and datasets (e.g., using an ...
Named Entity Recognition for Long-Tail Entities. ...
doi:10.5445/ir/1000131503
fatcat:mbhk5o4hpbbkjcr6j2gntvbeqy