A Deep Learning Approach to Contract Element Extraction
Ilias Chalkidis, Ion Androutsopoulos · Frontiers in artificial intelligence and applications · 2017
We explore how deep learning methods can be used for contract element extraction. We show that a BILSTM operating on word, POS tag, and token-shape embeddings outperforms the linear sliding-window classifiers of our previous work, without any manually written rules. Further improvements are observed by stacking an additional LSTM on top of the BILSTM, or by adding a CRF layer on top of the BILSTM. The stacked BILSTM-LSTM misclassifies fewer tokens, but the BILSTM-CRF combination performs better when methods are evaluated for their ability to extract entire, possibly multi-token contract elements.