Canary

Canary is an argument mining Python library. Argument Mining is the automated identifcation and extraction of argumentative data from natural language.

It should be noted that this software is currently under active development and is not fully functional or feature complete.

Installation

Canary will be installable through Pypi in the near-future. For the time being, it can be installed in the following manner:

https:

pip install git+https://github.com/chriswales95/Canary.git@development

ssh:

pip install git+ssh://[email protected]/chriswales95/Canary.git@development

Example Usage

Detecting an argument (true / false)

from canary.argument_pipeline import download_model, load_model, analyse_file

if __name__ == "__main__":
    # Download pretrained models from the web (unless you fancy creating them yourself)
    # Training the models takes a while so I'd advise against it.
    download_model("all")

    # load the detector
    detector = load_model("argument_detector")

    # outputs false
    print(detector.predict("cats are pretty lazy animals"))

    # outputs true
    print(detector.predict(
        "If a criminal knows that a person has a gun , they are much less likely to attempt a crime ."))

Analysing a full document

from canary.argument_pipeline import download_model, analyse_file
from canary.corpora import load_corpus
from pathlib import Path
if __name__ == "__main__":
    
    # Download all models
    download_model("all")
    
    # Load version 1 of the essay corpus. 
    essays = load_corpus("argument_annotated_essays_1", download_if_missing=True)
    if essays is not None:
        essays = [essay for essay in essays if Path(essay).suffix == ".txt"]
    
        # Analyse the first essay
        # essays[0] contains the absolute path to the first essay text file
        analysis = analyse_file(essays[0])

What kind of performance is Canary achieving?

Canary is currently still in development and performance is being improved as work continues.

Argument Detector

              precision    recall  f1-score   support

       False       0.85      0.86      0.86      2756
        True       0.86      0.85      0.85      2755

    accuracy                           0.86      5511
   macro avg       0.86      0.86      0.86      5511
weighted avg       0.86      0.86      0.86      5511

Argument Segmenter

              precision    recall  f1-score   support

           O     0.7936    0.7259    0.7583      9362
       Arg-B     0.7784    0.7765    0.7775      1235
       Arg-I     0.8761    0.9126    0.8939     19248

    accuracy                         0.8484     29845
   macro avg     0.8160    0.8050    0.8099     29845
weighted avg     0.8462    0.8484    0.8466     29845

Argument Component Predictor

              precision    recall  f1-score   support

       Claim       0.80      0.81      0.81      1150
  MajorClaim       0.90      0.98      0.94      1150
     Premise       0.90      0.82      0.86      1149

    accuracy                           0.87      3449
   macro avg       0.87      0.87      0.87      3449
weighted avg       0.87      0.87      0.87      3449

Link Predictor

              precision    recall  f1-score   support

      Linked       0.83      0.88      0.85      7417
  Not Linked       0.87      0.82      0.84      7311

    accuracy                           0.85     14728
   macro avg       0.85      0.85      0.85     14728
weighted avg       0.85      0.85      0.85     14728

Structure Predictor

              precision    recall  f1-score   support

     attacks       0.70      0.81      0.75      1106
    supports       0.76      0.64      0.69      1062

    accuracy                           0.72      2168
   macro avg       0.73      0.72      0.72      2168
weighted avg       0.73      0.72      0.72      2168