TSE-NER

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

Mesbah, S. (TU Delft Web Information Systems)
Lofi, C. (TU Delft Web Information Systems)
Valle Torre, M. (TU Delft Web Information Systems)
Bozzon, A. (TU Delft Web Information Systems)
Houben, G.J.P.M. (TU Delft Web Information Systems)

Vrandečić, D. (editor)
Bontcheva, K. (editor)
Suárez-Figueroa, M.C. (editor)
Presutti, V. (editor)
Celino, I. (editor)
Sabou, M. (editor)
Kaffee, L.M (editor)
Simperl, E. (editor)

2018

Named Entity Recognition and Typing (NER/NET) is a challenging task, especially with long-tail entities such as the ones found in scientific publications. These entities (e.g. “WebKB”, “StatSnowball”) are rare, often relevant only in specific knowledge domains, yet important for retrieval and exploration purposes. State-of-the-art NER approaches employ supervised machine learning models, trained on expensive typelabeled data laboriously produced by human annotators. A common workaround is the generation of labeled training data from knowledge bases; this approach is not suitable for long-tail entity types that are, by definition, scarcely represented in KBs.
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 type. We
introduce different strategies for training data extraction, semantic expansion, and result entity filtering.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.

http://resolver.tudelft.nl/uuid:91b0bf60-1304-4b2f-ba55-f58f04351381

https://doi.org/10.1007/978-3-030-00671-6_8

Springer, Cham

2019-02-18

978-3-030-00670-9

The Semantic Web – ISWC 2018: Proceedings of the 17th International Semantic Web Conference

ISWC 2018, 2018-10-08 → 2018-10-12, Monterey, CA, United States

Lecture Notes in Computer Science (LNCS), 11136

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2018 S. Mesbah, C. Lofi, M. Valle Torre, A. Bozzon, G.J.P.M. Houben