https://github.com/harshgeek4coder/multilabel_document_categorization_

This Repository consists of work done for performing Multilabel document categorization using Semi-Supervised Learning
https://github.com/harshgeek4coder/multilabel_document_categorization_

lda neural-network nlp nmf semi-supervised-learning svd-matrix-factorisation topic-modeling

Last synced: 12 days ago
JSON representation

This Repository consists of work done for performing Multilabel document categorization using Semi-Supervised Learning

• Host: GitHub
• URL: https://github.com/harshgeek4coder/multilabel_document_categorization_
• Owner: harshgeek4coder
• Created: 2021-07-08T17:08:15.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-07-23T18:19:02.000Z (over 3 years ago)
• Last Synced: 2024-10-04T11:59:40.011Z (3 months ago)
• Topics: lda, neural-network, nlp, nmf, semi-supervised-learning, svd-matrix-factorisation, topic-modeling
• Language: Python
• Homepage:
• Size: 13.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Multilabel Document Categorization

This Repository consists of work done for performing multilabel document categorization using both unsupervised and supervised learning.

This Work consists of two subtasks :

- Subtask I  : Unsupervised topic modelling

- Subtask II : Learning a supervised multi-Topic Classifier

### Running :

- Clone the Repo

- Activate your virtualenv.

- Run the following script in CLI :

```

pip install -r requirements.txt in your shell.

python main.py

```

- Please NOTE : 

  - You are supposed to put this file : ``` glove.6B.300d.txt ``` in this ```glove directory``` before running ```main.py```. Please Refer to this [Readme](https://github.com/harshgeek4coder/Multilabel_Document_Categorization_/blob/main/glove/README.md) for further instructions.

   - You would also have to download the following files [If Not Already Downloaded]:

   ```

   - nltk.download('stopwords')

   - nltk.download('punkt')

   - nltk.download('wordnet')

   ```

### Root Folder Structure : 

```

│   data_process.py

│   get_features.py

│   inference.py

│   main.py

│   post_process.py

│   prepare_embed_matrix.py

│   process_supervised_data.py

│   requirements.txt

│   save_n_load_state.py

│   supervised_models.py

│   tokenize_n_padding.py

│   unsupervised_models.py

│   utils.py

│

├───datasets

│       pre_processed_df.csv [This file will be automatically added once you run main.py]

│       sentisum-assessment-dataset.csv

│

└───glove

        glove.6B.300d.txt [After downloading and putting this file in glove directory.]

```

### Visual Analysis :

- Flow Chart of subtask 1 and 2 : 




- Model Architecture : 




- Model Performance : 




- Word Cloud : 




- Top 10 Words : 




### Results :

- For Unsupervised Multi Label Classifier - GridSearchCV on LDA Model : 


```

Fitting 3 folds for each of 12 candidates, totalling 36 fits

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed: 10.7min finished

Best model's params:  {'learning_decay': 0.75, 'learning_offset': 30, 'n_components': 12}

Best log likelihood score:  -83160.21471701778

Model perplexity:  55.26047238754284

```

- For Supervised Multi Label Classifier : 


```

|                             Model                             	| Accuracy  	|  Loss 	|

|:-------------------------------------------------------------:	|:---------:	|:-----:	|

|  Bidirectional LSTMs + Convnet 1-D + Latent Embeddings - 256-D 	|  76.22 %  	| 0.808 	|

|  Bidirectional LSTMs + Convnet 1-D + GloVe Embeddings - 300-D 	|  80.46 %  	| 0.667 	|

```

### Inferences :

For Supervised Inference : 




For Unsupervised Inference :