Deep Ensemble Learning for Legal Query Understanding
International Conference on Information and Knowledge Management
Legal query understanding is a complex problem that involves two natural language processing (NLP) tasks that needs to be solved together: (i) identifying intent of the user and (ii) recognizing entities within the queries. The problem equates to decomposing a legal query into its individual components and deciphering the underlying differences that can occur due to pragmatics. Identifying the desired intent and recognizing correct entities helps us return back relevant results to the user.
... Neural Networks (DNNs) have recently achieved great success surpassing traditional statistical approaches. In this work, we experiment with several DNN architectures towards legal query intent classification and entity recognition. Deep Neural architectures like Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRU) were applied and compared against one another both individually and as combinations. The models were also compared against machine learning (ML) and rule-based approaches. In this paper, we describe a methodology that integrates posterior probabilities produced by the best DNN models and create a stacked framework for combining the different predictors to improve prediction accuracy and F-measure for legal intent classification and entity recognition.