To understand the Soft Patterns model please refer to the following paper:
"SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines" by Roy Schwartz, Sam Thomson and Noah A. Smith, ACL 2018
Here is the link to their GitHub
The data used for this project can be find here
The data added manually is in the files data/Guardian_time.txt and data/Financial_tine.txt
To create the datasets download the AQUAINT training data and run the notebook "parse_time.ipynb"
For the project we used glove-300 as pretrained embedding
To get the embeddings files: run the function "read_embeddings" from sopa-master/data.py