The full Technical Report can be found directly in the repo under the name Scrutinizer_TR.pdf.

Data have been removed from the repo due to sensitivity reasons. Please contact us for more information.

Tested with Python 3.5.6 and Python 3.7.1

• src/tokenizer/tokenizer_driver.py
  • tokenizes input data, using spacy
• src/featurizer/featurizer_extractor.py
  • Can either be tfidf or word-embeddings
  • Each object will contain the features as an instance variable
• src/featurizer/sentence_embedding.py
  • Implements a scikit-learn transformer, which gives us the word (glove) embeddings, using spacy. I use average pooling here.
• src/classifier/classifier_linear_svm.py
  • Contains the Linear SVM model along with the sigmoid on top of it, which gives us calibrated probabilities for the topn predictions
  • The instance variable cv, determines the number of the cross validation folds. Hence, our input dataset needs to have at least cv number of samples per class, otherwise it will not work.
  • The default cv is 3, but one could try bigger values (4, 5), and see which gives better results experimentally.
• src/parser/dataset_parser.py
  • Contains logic, which creates the dataset for template and row_index predictions.
  • Look at src/templates/template_transformer.py for how the templates are created from the original Excel formulas.
  • Also has logic for combining features of sentences and claims
  • This class is a little "messy", which could be fixed a little later
• src/templates/template_transformer.py
  • Logic for creating templates from Excel formulas
  • Uses various Regexes (look at src/regex/regex.py), to filter (to some extent) Excel formulas
  • Uses pandas apply function to create a series of transformations for each row of the DataFrame

The experiments supported right now are src/experiments/exp_only_row_idx.py and src/experiments/exp_only_templates.py. You can see the code in both, to better see how I use the above classes for Tokenization, Feturization and Classification. The code is not very clean, and I expect to make it better as I go on.

You need to have:

(1) the data sepcified on top of each experiment python file. I.e the variable DATA_PATH, needs to correspond to a file. (2) The csv file needs to have the same format as the currect on this repo (data/main_annotated_dataset_12-16-2019.csv). (3) Need to have all the requirements from requirements.txt

Before you can run do:

• Create a Virtual Environment with python version 3.5.6 (although probably anything above that will do)
• python -m spacy download en_core_web_md
• pip install -r requirements.txt

From the root path of the directory run:

python -m src.experiments.exp_only_templates --num_runs 1 --cv 3 --min_samples_per_label 20 --topn 3

Where:

• num_runs: The number of times the task is run. The end accuracy numbers are the average of the accuracy of each run
• cv: Cross validation folds used for Classification
• min_samples_per_label: Min number of samples we keep for each label. Note that min_samples_per_label >= cv
• topn: Topn predictions to return