Team yeon-zi at SemEval-2019 task 4: Hyperpartisan news detection by de-noising weakly-labeled data

Nayeon Lee; Zihan Liu; Pascale Fung

doi:10.18653/v1/s19-2184

Team yeon-zi at SemEval-2019 task 4: Hyperpartisan news detection by de-noising weakly-labeled data

Nayeon Lee, Zihan Liu, Pascale Fung

Department of Electronic and Computer Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

16 Citations (Scopus)

Abstract

This paper describes our system submitted to SemEval-2019 Task 4: Hyperpartisan News Detection. We focus on removing the inherent noise in the hyperpartisanship dataset from both data-level and model-level by leveraging semi-supervised pseudo-labels and the state-of-the-art BERT model. Our model achieves 75.8% accuracy in the final by-article dataset without ensemble learning.

Original language	English
Title of host publication	NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop
Publisher	Association for Computational Linguistics (ACL)
Pages	1052-1056
Number of pages	5
ISBN (Electronic)	9781950737062
DOIs	https://doi.org/10.18653/v1/s19-2184
Publication status	Published - 2019
Event	13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States Duration: 6 Jun 2019 → 7 Jun 2019

Publication series

Name	NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop

Conference

Conference	13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Country/Territory	United States
City	Minneapolis
Period	6/06/19 → 7/06/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computational Linguistics

Access to Document

10.18653/v1/s19-2184

Cite this

Lee, N., Liu, Z., & Fung, P. (2019). Team yeon-zi at SemEval-2019 task 4: Hyperpartisan news detection by de-noising weakly-labeled data. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 1052-1056). (NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2184

Lee, Nayeon ; Liu, Zihan ; Fung, Pascale. / Team yeon-zi at SemEval-2019 task 4 : Hyperpartisan news detection by de-noising weakly-labeled data. NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop. Association for Computational Linguistics (ACL), 2019. pp. 1052-1056 (NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop).

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title = "Team yeon-zi at SemEval-2019 task 4: Hyperpartisan news detection by de-noising weakly-labeled data",

abstract = "This paper describes our system submitted to SemEval-2019 Task 4: Hyperpartisan News Detection. We focus on removing the inherent noise in the hyperpartisanship dataset from both data-level and model-level by leveraging semi-supervised pseudo-labels and the state-of-the-art BERT model. Our model achieves 75.8\% accuracy in the final by-article dataset without ensemble learning.",

author = "Nayeon Lee and Zihan Liu and Pascale Fung",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computational Linguistics; 13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 ; Conference date: 06-06-2019 Through 07-06-2019",

year = "2019",

doi = "10.18653/v1/s19-2184",

language = "English",

series = "NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop",

publisher = "Association for Computational Linguistics (ACL)",

pages = "1052--1056",

booktitle = "NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop",

}

Lee, N, Liu, Z & Fung, P 2019, Team yeon-zi at SemEval-2019 task 4: Hyperpartisan news detection by de-noising weakly-labeled data. in NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop. NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop, Association for Computational Linguistics (ACL), pp. 1052-1056, 13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019, Minneapolis, United States, 6/06/19. https://doi.org/10.18653/v1/s19-2184

Team yeon-zi at SemEval-2019 task 4: Hyperpartisan news detection by de-noising weakly-labeled data. / Lee, Nayeon; Liu, Zihan; Fung, Pascale.
NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop. Association for Computational Linguistics (ACL), 2019. p. 1052-1056 (NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Team yeon-zi at SemEval-2019 task 4

T2 - 13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019

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AU - Fung, Pascale

PY - 2019

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AB - This paper describes our system submitted to SemEval-2019 Task 4: Hyperpartisan News Detection. We focus on removing the inherent noise in the hyperpartisanship dataset from both data-level and model-level by leveraging semi-supervised pseudo-labels and the state-of-the-art BERT model. Our model achieves 75.8% accuracy in the final by-article dataset without ensemble learning.

UR - https://openalex.org/W2954447110

UR - https://www.scopus.com/pages/publications/85084291759

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DO - 10.18653/v1/s19-2184

M3 - Conference Paper published in a book

T3 - NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop

SP - 1052

EP - 1056

BT - NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop

PB - Association for Computational Linguistics (ACL)

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ER -

Lee N, Liu Z, Fung P. Team yeon-zi at SemEval-2019 task 4: Hyperpartisan news detection by de-noising weakly-labeled data. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop. Association for Computational Linguistics (ACL). 2019. p. 1052-1056. (NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop). doi: 10.18653/v1/s19-2184

Team yeon-zi at SemEval-2019 task 4: Hyperpartisan news detection by de-noising weakly-labeled data

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