We are pleased to announce that the submission is open for a new research topic in Frontiers in Artificial Intelligence - Natural Language Processing for Low-resource Languages and Domains.
Deadlines:
Abstract submission - 01 March 2022
Manuscript submission - 01 May 2022
We welcome any type of contributions related to low resource languages and domains. These include, but are not limited to:
*• NLP Techniques for low-resource languages or domains*
• Domain adaptation
• Transfer learning
• Zero-shot and few-shot learning
• Meta learning
• Knowledge distillation
• Multilingual and cross-lingual learning
• Data augmentation
*• Dataset and Evaluation for low-resource languages or domains*
• New benchmarks
• evaluation mechanism
• Multimodal resources Language resources (multilingual/monolingual, annotated/unannotated)
*• NLP Tasks for low-resource languages or domains*
• Bias, fairness and ethics in NLP
• Dialog and interactive systems
• Discourse and pragmatics
• Document analysis including text categorization, topic models, and retrieval
• Natural language generation
• Information extraction, text mining, and question answering
• Language-inclusive multimodal integration
• Machine translation
• Multilinguality
• Phonology, morphology, and word segmentation
• Semantics
• Text classification
• Fake news and hate-speech detection
• Sentiment analysis and opinion mining
• Social media analysis: Twitter, blogs, discussion forums, and other social media
• Speech, prosody, and spoken dialog
• Summarization
• Tagging, chunking, syntax, and parsing
For more information, visit https://www.frontiersin.org/research-topics/28809/natural-language-processing-for-low-resource-languages-and-domains
Regards,
The topic editors
Matthew Purver, Surangika Ranathunga, Ravi Shekhar, Rishemjit Kaur, En-Shiun Annie Lee -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 13368 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20220120/0ddcee0f/attachment.txt>