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Finding the best way to put media bias research into practice via an annotation app ...
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Movies with imaginary worlds cluster together because of exploration-related terms in plot summaries ...
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Finding the best way to put media bias research into practice through an annotation app ...
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Can distributional semantics explain performance on the false belief task? ...
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The First Gospel, the Gospel of the Poor: A New Reconstruction of Q and Resolution of the Synoptic Problem based on Marcion's Early Luke ...
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From Close to Distant Reading. Towards the Computational Analysis of "Liber Abbaci" ...
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From Close to Distant Reading. Towards the Computational Analysis of "Liber Abbaci" ...
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Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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The First Gospel, the Gospel of the Poor: A New Reconstruction of Q and Resolution of the Synoptic Problem based on Marcion's Early Luke ...
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Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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The glyph project: The distinctiveness of written characters — online crowdsourcing for a typology of letter shapes ...
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Formalization of AMR Inference via Hybrid Logic Tableaux ...
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Human-like learning of syntactic islands by neural networks ...
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Study 1 - Fred and his dog (revised with author vs respondent conditions) ...
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Recognition of Urdu sign language: a systematic review of the machine learning classification
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In: PeerJ Comput Sci (2022)
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Multi-label emotion classification of Urdu tweets
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In: PeerJ Comput Sci (2022)
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Abstract:
Urdu is a widely used language in South Asia and worldwide. While there are similar datasets available in English, we created the first multi-label emotion dataset consisting of 6,043 tweets and six basic emotions in the Urdu Nastalíq script. A multi-label (ML) classification approach was adopted to detect emotions from Urdu. The morphological and syntactic structure of Urdu makes it a challenging problem for multi-label emotion detection. In this paper, we build a set of baseline classifiers such as machine learning algorithms (Random forest (RF), Decision tree (J48), Sequential minimal optimization (SMO), AdaBoostM1, and Bagging), deep-learning algorithms (Convolutional Neural Networks (1D-CNN), Long short-term memory (LSTM), and LSTM with CNN features) and transformer-based baseline (BERT). We used a combination of text representations: stylometric-based features, pre-trained word embedding, word-based n-grams, and character-based n-grams. The paper highlights the annotation guidelines, dataset characteristics and insights into different methodologies used for Urdu based emotion classification. We present our best results using micro-averaged F1, macro-averaged F1, accuracy, Hamming loss (HL) and exact match (EM) for all tested methods.
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Keyword:
Computational Linguistics
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044368/ https://doi.org/10.7717/peerj-cs.896
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