SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media

Plaza-del-Arco, Flor Miriam; Molina-González, M. Dolores; Martin, Maite; Ureña‐López, L. Alfonso

doi:10.18653/v1/s19-2129

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…SA offers a valuable tool that helps to enhance the performance of machine learning classification systems, as shown in [26], [27]. A few recent studies have investigated the benefit of using SA features for HS detection.…”

Section: A Sentiment Analysis On Hate Speechmentioning

confidence: 99%

A Multi-Task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis

et al. 2021

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The rise of social media platforms has significantly changed the way our world communicates, and part of those changes includes a rise in inappropriate behaviors, such as the use of aggressive and hateful language online. Detecting such content is crucial to filtering or blocking inappropriate content on the Web. However, due to the huge amount of data posted every day, automatic methods are essential for identifying this type of content. Seeking to address this issue, the Natural Language Processing community is increasingly involved in testing a wide range of techniques for hate speech detection. While achieving promising results, these techniques consider hate speech detection as the sole optimization objective, without involving other related tasks such as polarity and emotion classification that are strongly linked to offensive behavior. In this paper, we propose the first Multi-task approach that leverages the shared affective knowledge to detect hate speech in Spanish tweets, using a well-known Transformer-based model. Our results show that the combination of both polarity and emotion knowledge helps to detect hate speech more accurately across datasets.

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Section: A Sentiment Analysis On Hate Speechmentioning

confidence: 99%

A Multi-Task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis

et al. 2021

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“…As a result, most of the studies and resources in offensive language research have been developed specifically for binary and multi-class classification tasks (Ranasinghe et al, 2019;Plaza-del Arco et al, 2019. However, other tasks such as Named Entity Recognition (NER) play an important role in this research and are essential to identify the entities that make a text toxic.…”

Section: Related Workmentioning

confidence: 99%

“…In order to help to track this type of comments and due to the amount of data generated every day on the Web, automatic systems based on Natural Language Processing (NLP) techniques are required. In particular, offensive language detection and analysis has become an important area of research in NLP, resulting in several studies that are contributing to combating this website phenomenon (Plaza-del Arco et al, 2019;Zampieri et al, 2019a;Ranasinghe et al, 2019;.…”

Section: Introductionmentioning

confidence: 99%

SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection

Plaza-del-Arco¹,

López-Úbeda²,

Ureña‐López³

et al. 2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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This paper describes the participation of SINAI team at Task 5: Toxic Spans Detection which consists of identifying spans that make a text toxic. Although several resources and systems have been developed so far in the context of offensive language, both annotation and tasks have mainly focused on classifying whether a text is offensive or not. However, detecting toxic spans is crucial to identify why a text is toxic and can assist human moderators to locate this type of content on social media. In order to accomplish the task, we follow a deep learning-based approach using a Bidirectional variant of a Long Short Term Memory network along with a stacked Conditional Random Field decoding layer (BiLSTM-CRF). Specifically, we test the performance of the combination of different pre-trained word embeddings for recognizing toxic entities in text. The results show that the combination of word embeddings helps in detecting offensive content. Our team ranks 29th out of 91 participants.

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“…Early studies explored traditional machine learning algorithms including Support Vector Machines, Logistic Regression, Random Forest, or Decision Trees, as well as the combination of different types of syntactic, lexical, semantic, and sentiment features (Chen et al, 2012;Nobata et al, 2016;Orȃsan, 2018;Plaza-del-Arco et al, 2019).…”

Section: Offensive Language Detectionmentioning

confidence: 99%

OffendES: A New Corpus in Spanish for Offensive Language Research

Plaza-del-Arco

Montejo-Ráez

Ureña‐López

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media

Cited by 11 publications

References 6 publications

A Multi-Task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis

A Multi-Task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis

SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection

OffendES: A New Corpus in Spanish for Offensive Language Research

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