Investigating how well contextual features are captured by bi-directional recurrent neural network models

Understand

Learning algorithms for natural language processing (NLP) tasks traditionally rely on manually defined relevant contextual features.

• On the other hand, neural network models using an only distributional representation of words have been successfully applied for several NLP tasks.
• Such models learn features automatically and avoid explicit feature engineering.
• Across several domains, neural models become a natural choice specifically when limited characteristics of data are known.