https://github.com/blurred-machine/sentence-inference

For every given pair of sentences -- (sentence-1, sentence-2), we need to determine if sentence-2 can be logically inferred given sentence-1.
https://github.com/blurred-machine/sentence-inference

jupyter-notebook machinelearning nlp python sentence-inference text-classification text-processing wordtovec

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For every given pair of sentences -- (sentence-1, sentence-2), we need to determine if sentence-2 can be logically inferred given sentence-1.

• Host: GitHub
• URL: https://github.com/blurred-machine/sentence-inference
• Owner: blurred-machine
• License: mit
• Created: 2020-05-26T16:21:35.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-05-27T15:02:45.000Z (over 4 years ago)
• Last Synced: 2024-11-11T07:35:01.281Z (2 months ago)
• Topics: jupyter-notebook, machinelearning, nlp, python, sentence-inference, text-classification, text-processing, wordtovec
• Language: Jupyter Notebook
• Homepage: https://jovian.ml/paras009/sentence-inference
• Size: 1.44 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Sentence-Inference

For every given pair of sentences -- (sentence-1, sentence-2), we need to determine if sentence-2 can be logically inferred given sentence-1.

## Dataset Description:

* `Sentence1`: String column of human entered text, Sentence 1

* `Sentence2`: String column of human entered text, Sentence 2

* `gold_label`: Categorical column inferring logical relation between sentence1 and sentence2

## Implementation 

* Length of document in sentence1: 

![Length of strings Sentence1](https://github.com/paras009/Sentence-Inference/blob/master/images/length_of_doc_s1.png)

* Length of document in sentence2: 

![Length of strings Sentence2](https://github.com/paras009/Sentence-Inference/blob/master/images/length_of_doc_s2.png)

* Heatmap of correlation between the features: 

![Heatmap](https://github.com/paras009/Sentence-Inference/blob/master/images/correlation_heatmap.png)

* Bidirectional LSTM Model performance(not good due to less data): 

![Loss](https://github.com/paras009/Sentence-Inference/blob/master/images/bidirectional_LSTM_model_performance_loss.png)

![Accuracy](https://github.com/paras009/Sentence-Inference/blob/master/images/bidirectional_LSTM_model_performance_accuracy.png)

* Selected model's performance for predicting the testing `gold_label`.

![MLPClassifier](https://github.com/paras009/Sentence-Inference/blob/master/images/MLPClassifier.PNG)

## Inference

* Since the dataset was very small, training a Neural network was not a good idea so I choose to move ahead with ML algorithms. 

* So, working on a large dataset can improve the learning.

* Advanced NLP techniques can be implemented to find the semantic relationship between both the sentences to get a better result.

* Due to lack of time I decided to follow this approach but with various iterations during the development, model's performance can increase significantly.

* `Data Cleaning` was done signifantly well but can be done using other approaches.

* `Feature engineering` is one important part which require good knowledge of NLP which can be worked upon in future.

* Dimensionality reduction based on experimentation on using `PCA` or `t-SNE` can be perfromed to optimize model performance and remove useless features.

* `Hypothesis testing` can be done in making useful decissions about the feature, whether they contribute in predicting right `gold_label` or not.

* `Word ebedding` can be implemented to get a better semantic relationship between words.

* Working with more better Neural Networks will be a better choice for this kind of problem, although `bidirectional LSTM` should perform well with large dataset.

* Finally once we get a good model performance over the data, we can implement hyperparameter tuning to tune those small knobs in the `bidirectional LSTM` model to extract the best performance out of it.

* for any suggestions contact me at [[email protected]]([email protected])