JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods

Yaakov HaCohen-Kerner; Ilan Meyrowitsch; Matan Fchima

JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods

Yaakov HaCohen-Kerner, Ilan Meyrowitsch, Matan Fchima

Jerusalem College of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper describes our submissions to SemEval-2022 subtask 4-A - 'Patronizing and Condescending Language Detection: Binary Classification". We developed different models for this subtask. We applied 11 supervised machine learning methods and 9 preprocessing methods. Our best submission was a model we built with BertForSequenceClassification. Our experiments indicate that pre-processing stage is a must for a successful model. The dataset for Subtask 1 is highly imbalanced. The F1-scores on the oversampled imbalanced training dataset were higher than the results on the original training dataset.

Original language	English
Title of host publication	SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop
Editors	Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Publisher	Association for Computational Linguistics (ACL)
Pages	519-524
Number of pages	6
ISBN (Electronic)	9781955917803
State	Published - 2022
Externally published	Yes
Event	16th International Workshop on Semantic Evaluation, SemEval 2022 - Seattle, United States Duration: 14 Jul 2022 → 15 Jul 2022

Publication series

Name	SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop

Conference

Conference	16th International Workshop on Semantic Evaluation, SemEval 2022
Country/Territory	United States
City	Seattle
Period	14/07/22 → 15/07/22

Bibliographical note

Publisher Copyright:
© 2022 Association for Computational Linguistics.

Cite this

HaCohen-Kerner, Y., Meyrowitsch, I., & Fchima, M. (2022). JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods. In G. Emerson, N. Schluter, G. Stanovsky, R. Kumar, A. Palmer, N. Schneider, S. Singh, & S. Ratan (Eds.), SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop (pp. 519-524). (SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop). Association for Computational Linguistics (ACL).

HaCohen-Kerner, Yaakov ; Meyrowitsch, Ilan ; Fchima, Matan. / JCT at SemEval-2022 Task 4-A : Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods. SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop. editor / Guy Emerson ; Natalie Schluter ; Gabriel Stanovsky ; Ritesh Kumar ; Alexis Palmer ; Nathan Schneider ; Siddharth Singh ; Shyam Ratan. Association for Computational Linguistics (ACL), 2022. pp. 519-524 (SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop).

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title = "JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods",

abstract = "This paper describes our submissions to SemEval-2022 subtask 4-A - 'Patronizing and Condescending Language Detection: Binary Classification{"}. We developed different models for this subtask. We applied 11 supervised machine learning methods and 9 preprocessing methods. Our best submission was a model we built with BertForSequenceClassification. Our experiments indicate that pre-processing stage is a must for a successful model. The dataset for Subtask 1 is highly imbalanced. The F1-scores on the oversampled imbalanced training dataset were higher than the results on the original training dataset.",

author = "Yaakov HaCohen-Kerner and Ilan Meyrowitsch and Matan Fchima",

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year = "2022",

language = "אנגלית",

series = "SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop",

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

pages = "519--524",

editor = "Guy Emerson and Natalie Schluter and Gabriel Stanovsky and Ritesh Kumar and Alexis Palmer and Nathan Schneider and Siddharth Singh and Shyam Ratan",

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HaCohen-Kerner, Y, Meyrowitsch, I & Fchima, M 2022, JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods. in G Emerson, N Schluter, G Stanovsky, R Kumar, A Palmer, N Schneider, S Singh & S Ratan (eds), SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop. SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop, Association for Computational Linguistics (ACL), pp. 519-524, 16th International Workshop on Semantic Evaluation, SemEval 2022, Seattle, United States, 14/07/22.

JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods. / HaCohen-Kerner, Yaakov; Meyrowitsch, Ilan; Fchima, Matan.
SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop. ed. / Guy Emerson; Natalie Schluter; Gabriel Stanovsky; Ritesh Kumar; Alexis Palmer; Nathan Schneider; Siddharth Singh; Shyam Ratan. Association for Computational Linguistics (ACL), 2022. p. 519-524 (SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper describes our submissions to SemEval-2022 subtask 4-A - 'Patronizing and Condescending Language Detection: Binary Classification". We developed different models for this subtask. We applied 11 supervised machine learning methods and 9 preprocessing methods. Our best submission was a model we built with BertForSequenceClassification. Our experiments indicate that pre-processing stage is a must for a successful model. The dataset for Subtask 1 is highly imbalanced. The F1-scores on the oversampled imbalanced training dataset were higher than the results on the original training dataset.

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HaCohen-Kerner Y, Meyrowitsch I, Fchima M. JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods. In Emerson G, Schluter N, Stanovsky G, Kumar R, Palmer A, Schneider N, Singh S, Ratan S, editors, SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop. Association for Computational Linguistics (ACL). 2022. p. 519-524. (SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop).

JCT at SemEval-2022 Task 4-A: Patronism Detection in Posts Written in English using Pre-processing Methods and various Machine Learning Methods

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Bibliographical note

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