Recognizing logical parts in legal texts using neural architectures
This paper proposes neural networks approaches to recognize logical parts in Vietnamese legal documents. We utilize four models based on recurrent neural networks including Long Short Term Memory (LSTM), Bidirectional LSTM and their combination with Conditional Random Fields. The experimental results on the Vietnamese Business Law data set shows the promising of this approach. Although, these approaches don't use any engineering features like traditional approaches, they can produce the state-of-the-art performance.