Transformer-Based Extractive Social Media Question Answering on TweetQA

Butt, Sabur; Ashraf, Noman; Siddiqui, Muhammad Hammad Fahim; Sidorov, Grigori; Gelbukh, Alexander

doi:10.13053/cys-25-1-3897

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The study also evaluated author profiling in French, Portuguese, and Italian, achieving similarly high accuracy rates. [10].…”

Section: State Of the Artmentioning

confidence: 99%

Prediction of Author’s Profile basing on Fine-Tuning BERT model

Bsir,

Khoufi,

Zrigui

2024

IJCAI

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The task of author profiling consists in specifying the infer-demographic features of the social networks' users by studying their published content or the interactions between them. In the literature, many research works were conducted to enhance the accuracy of the techniques used in this process. In fact, the existing methods can be divided into two types: simple linear models and complex deep neural network models. Among them, the transformer-based model exhibited the highest efficiency in NLP analysis in several languages (English, German, French, Turk, Arabic, etc.). Despite their good performance, these approaches do not cover author profiling analysis and, thus, should be further enhanced. So, we propose in this paper a new deep learning strategy by training a customized transformer-model to learn the optimal features of our dataset. In this direction, we fine-tune the model by using the transfer learning approach to improve the results with random initialization. We have achieved about 79% of accuracy by modifying model to apply the retraining process using PAN 2018 authorship dataset.Povzetek: Članek predstavlja novo metodo za napovedovanje avtorjevega profila, ki temelji na modelu Fine-Tuning BERT.

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“…The study also evaluated author profiling in French, Portuguese, and Italian, achieving similarly high accuracy rates. [10].…”

Section: State Of the Artmentioning

confidence: 99%

Prediction of Author’s Profile basing on Fine-Tuning BERT model

Bsir,

Khoufi,

Zrigui

2024

IJCAI

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“…Therefore, a multilabel emotion dataset for Urdu was long due and needed for understanding public emotions, especially applicable in natural language applications in disaster management, public policy, commerce, and public health. It should also be noted that emotion detection directly aids in solving other text related classification tasks such as sentiment analysis (Khan et al, 2021), human aggressiveness and emotion detection (Bashir et al, 2019; Ameer et al, 2021, humor detection (Weller and Seppi, 2019), question answering and fake news detection (Butt et al, 2021a; Ashraf et al, 2021a, depression detection (Mustafa et al, 2020), and abusive and threatening language detection (Ashraf et al, 2021b, 2020; Butt et al, 2021b; Amjad et al, 2021.…”

Section: Computer Sciencementioning

confidence: 99%

Peer Review #3 of "Multi-label emotion classification of Urdu tweets (v0.1)"

2022

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“…we evaluated our models using Recall (R), Precision (P), and macro F1-measure. These machine and deep learning classifiers have shown competitive performance for several NLP tasks (Devlin et al, 2019;Kim, 2014;Hochreiter & Schmidhuber, 1997;Breiman, 2001;Kohavi, 1995;Bashir et al, 2019;Khan et al, 2021;Butt et al, 2021b;Karande et al, 2021;Ashraf et al, 2021;Ameer et al, 2021;Butt et al, 2021a).…”

Section: Benchmarksmentioning

confidence: 99%

Abusive language detection in youtube comments leveraging replies as conversational context

Ashraf

Zubiaga

Gelbukh

2021

PeerJ Computer Science

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Nowadays, social media experience an increase in hostility, which leads to many people suffering from online abusive behavior and harassment. We introduce a new publicly available annotated dataset for abusive language detection in short texts. The dataset includes comments from YouTube, along with contextual information: replies, video, video title, and the original description. The comments in the dataset are labeled as abusive or not and are classified by topic: politics, religion, and other. In particular, we discuss our refined annotation guidelines for such classification. We report a number of strong baselines on this dataset for the tasks of abusive language detection and topic classification, using a number of classifiers and text representations. We show that taking into account the conversational context, namely, replies, greatly improves the classification results as compared with using only linguistic features of the comments. We also study how the classification accuracy depends on the topic of the comment.

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Transformer-Based Extractive Social Media Question Answering on TweetQA

Cited by 11 publications

References 14 publications

Prediction of Author’s Profile basing on Fine-Tuning BERT model

Prediction of Author’s Profile basing on Fine-Tuning BERT model

Peer Review #3 of "Multi-label emotion classification of Urdu tweets (v0.1)"

Abusive language detection in youtube comments leveraging replies as conversational context

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