University of Pittsburgh

Semi-supervised Knowledge Graph Cultivation for Contract Segment Classification

Graduate Student
Date: 
Friday, February 5, 2021 - 12:30pm - 1:00pm

Written contracts are formally legal agreements between parties and are a fundamental framework for goods exchange or services. Contract segment classification is an important problem in legal contract analysis allowing for useful insights such as rights and obligations extraction. Most of existing work only tried BoW machine learning classifiers (e.g. SVM) to small datasets consisting of a few hundred of sentences due to the rarity of such annotated data. In this paper however, we propose an end-to-end semi-supervised generative model, by leveraging a legal knowledge graph and unlabeled data. The proposed model can classify the contract clauses, while activating a sub-graph on the knowledge graph and expand the knowledge graph simultaneously. Experiment results on a real-world contract dataset show that our proposed model achieves improvement compared to state-of-the-art baselines.

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