• Xin Zeng
• Yujie Cai
• Jiahui Tang
• Yingchen Liu

In this project, we would use a dataset from Harvard Law School Case Law Access Project. The main data sets we used here for modeling are the latest case metadata and text for each case by the jurisdiction in North Carolina. The data basically consisted of two parts: header and opinion. We performed a PCA analysis to determine whether we should use the header as a predictor. We then conduct various data preprocessing methods to handle text data.

We then describe our modeling approach and go over the logical description of modeling decisions and process following the order of baseline model, improved baseline models (LexNLP, Topic Modeling, Word2Vec), Text Classification Method (FFNN / LSTM / BERT with FFNN/LSTM in the Appendix: Models), BERT for Document Similarity, and lastly, our proposed LawPairBERT Model.

Our LawPairBERT model has two version, one with BERT Embedding and another one with both BERT and Graph Embedding. Our LawPairBERT model has many advantages which overcomes the problems and concerns of other models we experiment. LawPairBERT model could directly captures the citation relationship among each pair of law cases. And it achieves high accuracy in predicting both true and false condition of citation. Moreover, through the Pairs Representation we designed using Imbalanced Random Concatenation, we could reduce computational cost while remaining high accuracy. We regard it as a good contribution and treat it as our final deliverable model.