Time Expression Detection Using Soft Patterns

Sources

To understand the Soft Patterns model please refer to the following paper:

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

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
data		data
sopa_master		sopa_master
README.md		README.md
bilstm.ipynb		bilstm.ipynb
cnn.ipynb		cnn.ipynb
dan.ipynb		dan.ipynb
gridsearch_cnn.py		gridsearch_cnn.py
gridsearch_sopa.py		gridsearch_sopa.py
interpretability.ipynb		interpretability.ipynb
parse_time.ipynb		parse_time.ipynb
pattern_extractor_clf.ipynb		pattern_extractor_clf.ipynb
sopa-bestmodel.ipynb		sopa-bestmodel.ipynb