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On the Etymologies of Kinnabari, Kinnamon, Kinawar, Kustumbari, Koriandron, Mercury et al. ...
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On the Etymologies of Kinnabari, Kinnamon, Kinawar, Kustumbari, Koriandron, Mercury et al. ...
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Πολυγλωσσία και γλωσσομάθεια στα Βαλκάνια (β΄ μισό 15ου-μέσα 19ου αιώνα) ... : Multilingualism and λanguage-λearning in the Balkans (second half of 15th –middle of 19th centuries) ...
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First Event-Related Potentials Evidence of Auditory Morphosyntactic Processing in a Subject-Object-Verb Nominative-Accusative Language (Farsi)
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An Overview of Life and Works of Jami & His Perception of Love
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In: Journal of Islamic and Middle Eastern Multidisciplinary Studies (2021)
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LSCP: Enhanced Large Scale Colloquial Persian Language Understanding
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In: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020) ; 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02628956 ; 12th Language Resources and Evaluation Conference, European Language Resources Association, May 2020, Marseille, France. pp.6323-6327 ; https://www.aclweb.org/anthology/2020.lrec-1.776/ (2020)
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Abstract:
6 pages, 2 figures, 3 tables, Proceedings of The 12th Language Resources and Evaluation Conference (LREC 2020), Learn more about this project at https://iasbs.ac.ir/~ansari/lscp ; International audience ; Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a “Large Scale Colloquial Persian Dataset” (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [INFO]Computer Science [cs]; [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; Colloquial Persian Language; Informal Language; Large Scale Language Understanding; Multilingual; Persian Corpus
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URL: https://hal.archives-ouvertes.fr/hal-02628956/document
https://hal.archives-ouvertes.fr/hal-02628956
https://hal.archives-ouvertes.fr/hal-02628956/file/abdikhojasteh_LSCP_2020.lrec-1.776.pdf
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