Identification of offensive language in Urdu using semantic and embedding models

Hussain, Sajid; Malik, Muhammad Shahid Iqbal; Masood, Nayyer

doi:10.7717/peerj-cs.1169

Cited by 10 publications

(5 citation statements)

References 35 publications

(44 reference statements)

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“…In addition, it can generate a vector of a specific length for each word by taking a sentence as input. Word2vec has demonstrated significant performance in similar NLP tasks ( Ali & Malik, 2023 ; Hussain, Malik & Masood, 2022 ; Younas, Malik & Ignatov, 2023 ). The skip-gram and continuous bag of words (CBOW) are the two algorithms supported by the word2vec model to generate word embeddings.…”

Section: Framework Methodologymentioning

confidence: 99%

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Malik,

Younas,

Jamjoom

et al. 2024

PeerJ Computer Science

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Identification of infrastructure and human damage assessment tweets is beneficial to disaster management organizations as well as victims during a disaster. Most of the prior works focused on the detection of informative/situational tweets, and infrastructure damage, only one focused on human damage. This study presents a novel approach for detecting damage assessment tweets involving infrastructure and human damages. We investigated the potential of the Bidirectional Encoder Representations from Transformer (BERT) model to learn universal contextualized representations targeting to demonstrate its effectiveness for binary and multi-class classification of disaster damage assessment tweets. The objective is to exploit a pre-trained BERT as a transfer learning mechanism after fine-tuning important hyper-parameters on the CrisisMMD dataset containing seven disasters. The effectiveness of fine-tuned BERT is compared with five benchmarks and nine comparable models by conducting exhaustive experiments. The findings show that the fine-tuned BERT outperformed all benchmarks and comparable models and achieved state-of-the-art performance by demonstrating up to 95.12% macro-f1-score, and 88% macro-f1-score for binary and multi-class classification. Specifically, the improvement in the classification of human damage is promising.

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Section: Framework Methodologymentioning

confidence: 99%

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Malik,

Younas,

Jamjoom

et al. 2024

PeerJ Computer Science

Self Cite

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“…Word2vec word embedding model has shown state-ofthe-art performance in many classification tasks related to the NLP domain [36][37][38]. There are two methods supported by word2vec to generate word embeddings; skip-gram and CBOW.…”

Section: Word2vecmentioning

confidence: 99%

Fine-Tuning Transformer Models Using Transfer Learning for Multilingual Threatening Text Identification

Rehan,

Malik,

Jamjoom

2023

IEEE Access

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Threatening content detection on social media has recently gained attention. There is very limited work regarding threatening content detection in low-resource languages, especially in Urdu. Furthermore, previous work explored only mono-lingual approaches, and multi-lingual threatening content detection was not studied. This research addressed the task of Multi-lingual Threatening Content Detection (MTCD) in Urdu and English languages by exploiting transfer learning methodology with fine-tuning techniques. To address the multi-lingual task, we investigated two methodologies: 1) Joint multi-lingual, and 2) Joint-translated method. The former approach employs the concept of building a universal classifier for different languages whereas the latter approach applies the translation process to transform the text into one language and then perform classification. We explore the Multilingual Representations for Indian Languages (MuRIL) and Robustly Optimized BERT Pre-Training Approach (RoBERTa) with fine-tuning that already demonstrated state-of-the-art in capturing the contextual and semantic characteristics within the text. For hyper-parameters, manual search and grid search strategies are utilized to find the optimum values. Various experiments are performed on bi-lingual English and Urdu datasets and findings revealed that the proposed methodology outperformed the baselines and showed benchmark performance. The RoBERTa model achieved the highest performance by demonstrating 92% accuracy and 90% macro f1-score with the joint multi-lingual approach.

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“…Several machine learning techniques have been used to detect the offensive language in Urdu, as Hussain, Malik & Masood (2022) used embedding to train the classifier, Humayoun (2022) used feature combination, Ali et al (2022) used transfer learning, and Das, Banerjee & Mukherjee (2022) used data bootstrapping Table 1 shows machine learning (ML) and deep learning (DL) techniques for classification, datasets used for the model, pre-processing technique and feature selection ( i.e. , term frequency-inverse document frequency (TFIDF), n-gram, lexicon or word embeddings).…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of offensive terms in resource-poor language using machine learning algorithms

Raza,

Mahoto,

Hamdi

et al. 2023

PeerJ Computer Science

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The use of offensive terms in user-generated content on different social media platforms is one of the major concerns for these platforms. The offensive terms have a negative impact on individuals, which may lead towards the degradation of societal and civilized manners. The immense amount of content generated at a higher speed makes it humanly impossible to categorise and detect offensive terms. Besides, it is an open challenge for natural language processing (NLP) to detect such terminologies automatically. Substantial efforts are made for high-resource languages such as English. However, it becomes more challenging when dealing with resource-poor languages such as Urdu. Because of the lack of standard datasets and pre-processing tools for automatic offensive terms detection. This paper introduces a combinatorial pre-processing approach in developing a classification model for cross-platform (Twitter and YouTube) use. The approach uses datasets from two different platforms (Twitter and YouTube) the training and testing the model, which is trained to apply decision tree, random forest and naive Bayes algorithms. The proposed combinatorial pre-processing approach is applied to check how machine learning models behave with different combinations of standard pre-processing techniques for low-resource language in the cross-platform setting. The experimental results represent the effectiveness of the machine learning model over different subsets of traditional pre-processing approaches in building a classification model for automatic offensive terms detection for a low resource language, i.e., Urdu, in the cross-platform scenario. In the experiments, when dataset D1 is used for training and D2 is applied for testing, the pre-processing approach named Stopword removal produced better results with an accuracy of 83.27%. Whilst, in this case, when dataset D2 is used for training and D1 is applied for testing, stopword removal and punctuation removal were observed as a better preprocessing approach with an accuracy of 74.54%. The combinatorial approach proposed in this paper outperformed the benchmark for the considered datasets using classical as well as ensemble machine learning with an accuracy of 82.9% and 97.2% for dataset D1 and D2, respectively.

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Identification of offensive language in Urdu using semantic and embedding models

Cited by 10 publications

References 35 publications

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Categorization of tweets for damages: infrastructure and human damage assessment using fine-tuned BERT model

Fine-Tuning Transformer Models Using Transfer Learning for Multilingual Threatening Text Identification

Detection of offensive terms in resource-poor language using machine learning algorithms

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