SATP_12.20

1. Annotation pipeline

1_annotation_pipeline-12.21.ipynb

2. Prepare our training/ testing data

2_Preprocess-Data-12.27.ipynb

3. BiLSTM Baseline Deep Learning Model

3_BiLSTM.ipynb

4. BERT

4_transformer_reduced_bert.ipynb

5. Baselines: Traditional Machine learning models (SVM, LR) using 2-step pipeline.

5_Baselines_reduced-2-step.ipynb

6. HAN

6.1 Create word vector and dataloader from train.csv.

python create_input_files.py

6.2 Train and eval HAN model

python HAN_end2end.py

Result:

	Accuracy	Precision	Recall	F1	Exact Matching Ratio
One vs Rest	26.07	87.19	27.40	28.04	82.20
Binary Relevance	26.65	86.08	28.29	28.40	82.47
Class Chain	27.93	83.47	29.78	29.32	82.87
Label Powerset	28.86	85.53	30.51	30.05	83.14
BiLSTM	37.11	53.81	58.51	42.12	76.23
HAN	52.98	65.64	74.82	55.78	83.07
BERT	62.52	74.73	80.01	64.59	87.23