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we have implemented a relation extraction with the help of a classifier that can classify a given sentence to one of the four classes of publisher, performer, director, character.

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zamaniali1995/relation-extraction

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📚 3-rd Party Libraries

You do not need to list nltk and pandas here.

  • main.py L:[4] used [sklearn.model_selection] for [importing KFold class].
  • main.py L:[5] used [sklearn.metrics] for [importing confusion_matrix class].
  • main.py L:[34] used [KFold] for [defining the kf object for cross validation purposes].
  • main.py L:[232, 235, 236] used [confusion_matrix] for [constructing the confusion matrix].

🏃 Execution

Example usage: use the following command in the current directory.

python3 src/main.py --train data/train.csv --test data/test.csv --output output/test.csv

📊 Data

The data can be found in data/train.txt,and the in-domain test data can be found in data/test.txt.

✍️ Results

Accuracy

Accuracy

Confusion Matrix

Confusion matrix

Class 1 (Characters)

Class 1 (Characters)

Class 2 (Director)

Class 2 (Director)

Class 3 (Performer)

Class 3 (Performer)

Class 4 (Publisher)

Class 4 (Publisher)

Precision and recall

Class 4 (Publisher)

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we have implemented a relation extraction with the help of a classifier that can classify a given sentence to one of the four classes of publisher, performer, director, character.

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