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TSE-NER: An Iterative Approach for Long-Tail Entity Extraction in Scientific Publications [chapter]

Sepideh Mesbah, Christoph Lofi, Manuel Valle Torre, Alessandro Bozzon, Geert-Jan Houben
2018 Lecture Notes in Computer Science  
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

Michael Färber, Alexander Albers, Felix Schüber
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