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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...149 Hits 1 – 20 of 2.974 1 Finding the best way to put media bias research into practice via an annotation app ... Hornung, Tilman. - : Open Science Framework, 2022 BASE Show details 2 Movies with imaginary worlds cluster together because of exploration-related terms in plot summaries ... Dubourg, Edgar. - : Open Science Framework, 2022 BASE Show details 3 Finding the best way to put media bias research into practice through an annotation app ... Hornung, Tilman. - : Open Science Framework, 2022 BASE Show details 4 Spatial representations without spatial computations ... Gatti, Daniele. - : Open Science Framework, 2022 BASE Show details 5 CompLanguageLearning ... Koplenig, Alexander. - : Open Science Framework, 2022 BASE Show details 6 Can distributional semantics explain performance on the false belief task? ... Trott, Sean. - : Open Science Framework, 2022 BASE Show details 7 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 ... Bilby, Mark G.. - : Zenodo, 2022 BASE Show details 8 From Close to Distant Reading. Towards the Computational Analysis of "Liber Abbaci" ... Ricci, Letizia; Grotto, Francesco; Fantoli, Margherita. - : Zenodo, 2022 BASE Show details 9 From Close to Distant Reading. Towards the Computational Analysis of "Liber Abbaci" ... Ricci, Letizia; Grotto, Francesco; Fantoli, Margherita. - : Zenodo, 2022 BASE Show details 10 Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Giovanna Maria Dora; Bhattacharya, Rohit. - : Underline Science Inc., 2022 BASE Show details 11 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 ... Bilby, Mark G.. - : Zenodo, 2022 BASE Show details 12 Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Mamidi, Radhika; Pant, Kartikey. - : Underline Science Inc., 2022 BASE Show details 13 Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Hou, Yifan; Sachan, Mrinmaya. - : Underline Science Inc., 2022 BASE Show details 14 The glyph project: The distinctiveness of written characters — online crowdsourcing for a typology of letter shapes ... Morin, Olivier. - : Open Science Framework, 2022 BASE Show details 15 Formalization of AMR Inference via Hybrid Logic Tableaux ... Goldner, Eli Tecumseh. - : Brandeis University, 2022 BASE Show details 16 Human-like learning of syntactic islands by neural networks ... Suijkerbuijk, Michelle. - : Open Science Framework, 2022 BASE Show details 17 Study 1 - Fred and his dog (revised with author vs respondent conditions) ... Shorr, Ardon. - : Open Science Framework, 2022 BASE Show details 18 Lingue classiche e linguistica computazionale Passarotti, Marco (orcid:0000-0002-9806-7187). - 2022 BASE Show details 19 Recognition of Urdu sign language: a systematic review of the machine learning classification Zahid, Hira; Rashid, Munaf; Hussain, Samreen; Azim, Fahad; Syed, Sidra Abid; Saad, Afshan In: PeerJ Comput Sci (2022) Abstract: BACKGROUND AND OBJECTIVE: Humans communicate with one another using language systems such as written words or body language (movements), hand motions, head gestures, facial expressions, lip motion, and many more. Comprehending sign language is just as crucial as learning a natural language. Sign language is the primary mode of communication for those who have a deaf or mute impairment or are disabled. Without a translator, people with auditory difficulties have difficulty speaking with other individuals. Studies in automatic recognition of sign language identification utilizing machine learning techniques have recently shown exceptional success and made significant progress. The primary objective of this research is to conduct a literature review on all the work completed on the recognition of Urdu Sign Language through machine learning classifiers to date. MATERIALS AND METHODS: All the studies have been extracted from databases, i.e., PubMed, IEEE, Science Direct, and Google Scholar, using a structured set of keywords. Each study has gone through proper screening criteria, i.e., exclusion and inclusion criteria. PRISMA guidelines have been followed and implemented adequately throughout this literature review. RESULTS: This literature review comprised 20 research articles that fulfilled the eligibility requirements. Only those articles were chosen for additional full-text screening that follows eligibility requirements for peer-reviewed and research articles and studies issued in credible journals and conference proceedings until July 2021. After other screenings, only studies based on Urdu Sign language were included. The results of this screening are divided into two parts; (1) a summary of all the datasets available on Urdu Sign Language. (2) a summary of all the machine learning techniques for recognizing Urdu Sign Language. CONCLUSION: Our research found that there is only one publicly-available USL sign-based dataset with pictures versus many character-, number-, or sentence-based publicly available datasets. It was also concluded that besides SVM and Neural Network, no unique classifier is used more than once. Additionally, no researcher opted for an unsupervised machine learning classifier for detection. To the best of our knowledge, this is the first literature review conducted on machine learning approaches applied to Urdu sign language. Keyword: Computational Linguistics URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044266/ https://doi.org/10.7717/peerj-cs.883 BASE Hide details 20 Multi-label emotion classification of Urdu tweets Ashraf, Noman; Khan, Lal; Butt, Sabur... In: PeerJ Comput Sci (2022) BASE Show details Page: 1 2 3 4 5...149

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