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Table 8 Rule-based vs learning-based techniques

From: An analytical study of information extraction from unstructured and multidimensional big data

Rule-based approaches Learning-based approaches
Interpretable and suitable for rapid development and domain transfer [114] The performance of machine learning approaches is better in terms of precision and recall but appropriate feature selection is important [115]
Humans and machines can contribute to the same model. So it is easy to incorporate domain knowledge [114]
Heavily rely on domain thesauri [11]
Generating training data is time consuming in learning-based approaches whereas rule-based approaches require pre-defined vocabularies [116]
Although rule-based systems require domain knowledge and are time consuming, results proved that these are more reliable and useful for automated processing [117] No experts are required and system can be developed quickly with relatively low cost [118]
Declarative [119] Adaptable [119]
Requires tiresome manual work [118] Less manual effort [118]
Highly transparent and expressive Higher portability than rule-based [9]