Methods to overcome sparsity of data in NLP
The lack of data results in the failure to improve potential language applications in NLP. Below are some techniques for overcoming data sparsity.
- Generating Additional Labeled Data
- Transfer Learning
- Ideas from Low-Resource Machine Learning in Non-NLP Communities
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Natural language processing
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Methods to overcome sparsity of data in NLP
Analyzing Data Scarcity in NLP Projects
An NLP team is developing a text summarization model for medical research papers in English. Although English is a high-resource language with vast amounts of general text available online, the team has only managed to collect 500 research papers with corresponding expert-written summaries. Which statement best analyzes this situation?
An NLP project is considered 'low-resource' when it suffers from data scarcity. Match each dimension of resource scarcity with its corresponding description and impact on model development.