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Coner : A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications. / Vliegenthart, Daniel; Mesbah, Sepideh; Lofi, Christoph; Aizawa, Akiko; Bozzon, Alessandro.

Digital Libraries for Open Knowledge : 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Proceedings. ed. / Antoine Doucet; Antoine Isaac; Koraljka Golub; Trond Aalberg; Adam Jatowt. Cham : Springer, 2019. p. 3-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11799 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Vliegenthart, D, Mesbah, S, Lofi, C, Aizawa, A & Bozzon, A 2019, Coner: A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications. in A Doucet, A Isaac, K Golub, T Aalberg & A Jatowt (eds), Digital Libraries for Open Knowledge : 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11799 LNCS, Springer, Cham, pp. 3-17, 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Oslo, Norway, 9/09/19. https://doi.org/10.1007/978-3-030-30760-8_1

APA

Vliegenthart, D., Mesbah, S., Lofi, C., Aizawa, A., & Bozzon, A. (2019). Coner: A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications. In A. Doucet, A. Isaac, K. Golub, T. Aalberg, & A. Jatowt (Eds.), Digital Libraries for Open Knowledge : 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Proceedings (pp. 3-17). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11799 LNCS). Cham: Springer. https://doi.org/10.1007/978-3-030-30760-8_1

Vancouver

Vliegenthart D, Mesbah S, Lofi C, Aizawa A, Bozzon A. Coner: A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications. In Doucet A, Isaac A, Golub K, Aalberg T, Jatowt A, editors, Digital Libraries for Open Knowledge : 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Proceedings. Cham: Springer. 2019. p. 3-17. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-30760-8_1

Author

Vliegenthart, Daniel ; Mesbah, Sepideh ; Lofi, Christoph ; Aizawa, Akiko ; Bozzon, Alessandro. / Coner : A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications. Digital Libraries for Open Knowledge : 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019, Proceedings. editor / Antoine Doucet ; Antoine Isaac ; Koraljka Golub ; Trond Aalberg ; Adam Jatowt. Cham : Springer, 2019. pp. 3-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{e3d634e46ba94ab4b80affaefc527091,
title = "Coner: A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications",
abstract = "Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific scientific publications is a challenging task, as typically the extensive training data and test data for fine-tuning NER algorithms is lacking. Recent approaches presented promising solutions relying on training NER algorithms in an iterative weakly-supervised fashion, thus limiting human interaction to only providing a small set of seed terms. Such approaches heavily rely on heuristics in order to cope with the limited training data size. As these heuristics are prone to failure, the overall achievable performance is limited. In this paper, we therefore introduce a collaborative approach which incrementally incorporates human feedback on the relevance of extracted entities into the training cycle of such iterative NER algorithms. This approach, called Coner, allows to still train new domain specific rare long-tail NER extractors with low costs, but with ever increasing performance while the algorithm is actively used in an application.",
author = "Daniel Vliegenthart and Sepideh Mesbah and Christoph Lofi and Akiko Aizawa and Alessandro Bozzon",
year = "2019",
doi = "10.1007/978-3-030-30760-8_1",
language = "English",
isbn = "978-3-030-30759-2",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "3--17",
editor = "Antoine Doucet and Antoine Isaac and Koraljka Golub and Trond Aalberg and Adam Jatowt",
booktitle = "Digital Libraries for Open Knowledge",

}

RIS

TY - GEN

T1 - Coner

T2 - A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications

AU - Vliegenthart, Daniel

AU - Mesbah, Sepideh

AU - Lofi, Christoph

AU - Aizawa, Akiko

AU - Bozzon, Alessandro

PY - 2019

Y1 - 2019

N2 - Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific scientific publications is a challenging task, as typically the extensive training data and test data for fine-tuning NER algorithms is lacking. Recent approaches presented promising solutions relying on training NER algorithms in an iterative weakly-supervised fashion, thus limiting human interaction to only providing a small set of seed terms. Such approaches heavily rely on heuristics in order to cope with the limited training data size. As these heuristics are prone to failure, the overall achievable performance is limited. In this paper, we therefore introduce a collaborative approach which incrementally incorporates human feedback on the relevance of extracted entities into the training cycle of such iterative NER algorithms. This approach, called Coner, allows to still train new domain specific rare long-tail NER extractors with low costs, but with ever increasing performance while the algorithm is actively used in an application.

AB - Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific scientific publications is a challenging task, as typically the extensive training data and test data for fine-tuning NER algorithms is lacking. Recent approaches presented promising solutions relying on training NER algorithms in an iterative weakly-supervised fashion, thus limiting human interaction to only providing a small set of seed terms. Such approaches heavily rely on heuristics in order to cope with the limited training data size. As these heuristics are prone to failure, the overall achievable performance is limited. In this paper, we therefore introduce a collaborative approach which incrementally incorporates human feedback on the relevance of extracted entities into the training cycle of such iterative NER algorithms. This approach, called Coner, allows to still train new domain specific rare long-tail NER extractors with low costs, but with ever increasing performance while the algorithm is actively used in an application.

UR - http://www.scopus.com/inward/record.url?scp=85072851105&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-30760-8_1

DO - 10.1007/978-3-030-30760-8_1

M3 - Conference contribution

SN - 978-3-030-30759-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 3

EP - 17

BT - Digital Libraries for Open Knowledge

A2 - Doucet, Antoine

A2 - Isaac, Antoine

A2 - Golub, Koraljka

A2 - Aalberg, Trond

A2 - Jatowt, Adam

PB - Springer

CY - Cham

ER -

ID: 62209661