NLP-Relation-Extraction-Classification

We classify the relation between head and tail entity using a Naive Bayes model based on bag-of-words (BoW) features.

Data

Input data: Two csv files: train.csv and test.csv, adapted from the FewRel dataset. Each csv file contains 5 columns:

Column	Example	Description
row_id	435	The unique row id.
tokens	Trapped and Deceived is a 1994 television film directed by Robert Iscove .	The tokenized sentence, separated by a single space.
relation	director	The correct relation (original label).
head_pos	0 1 2	Position of the head entity (Trapped and Deceived). Indices start with 0 and are separated by a single space.
tail_pos	11 12	Position of the tail entity (Robert Iscove).

Tasks

Task 1: Classify Text

• We wrote a program that trains a Naive Bayes classifier for the 4-class classification problem at hand. It processes texts and classify them as belonging to one of the following classes: publisher, director, performer, and characters. The NB classifier is implemented from scratch.
• The program should trains the model with the training file, and print the training accuracy (using 3-fold cross validation). After training the model, the program takes the test file as input, makes predictions using the model, writes the predictions to files, and prints the accuracy on the test set.

Task 2: Improve the Classifier

• We did one round of improvements based on our error analysis.

Output

The output should be a csv file containing the following 3 columns in the same order: original_label, output_label, and row_id.

Report and Results

Further details and results can be found here

Contributors

Leen Alzebdeh @Leen-Alzebdeh

Sukhnoor Khehra @Sukhnoor-K

Resources Consulted

• Chapter 4 of the Speech and Language Processing textbook.
• Used ChatGPT to help write the top_mi_selection and calculate_mutual_information functions.
• Used Github Copilot

Libraries

• main.py L:[line] used [Library Name] for [reason].
• main.py L:[5, 15] used argparse for parsing command line arguments.
• main.py L:[75, 173] used csv to read anf write csv files.
• main.py L[191] used random to shuffle training data.
• main.py L[132, 137] used math to find logs of probabilities.
• main.py L[56, 57] used statistics to find the mean and standard deviation of the training accuracy.
• main.py L[103, 104, 117, 119, 150, 207] used collections to use defaultdict.

Instructions to execute code

Ensure Python is installed, as well as the Python Standard Library.

To run the improved mode, add the flag -i.

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 -i for the improved model.

python3 src/main.py --train data/train.csv --test data/test.csv --output output/test.csv for the original model.