Implementation of Tree Structured LSTM and Attention Mechanism Models for the task of Sentiment Analysis on Stanford Sentiment Treebank

In this project we have implemented following models:

1. Linear LSTM model (baseline)
2. Tree Structured LSTM model taking reference from Kai Sheng Tai's paper Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks.
3. Tree Structure LSTM with Attention Mechanism.

• PyTorch Deep learning library for the implementaion of Neural Models
• Tensorflow Deep learning library by Google for the implementaion of Neural Models
• tqdm: display progress bar
• Java >= 8 (for Stanford CoreNLP utilities i.e. Stanford Parsers)
• Python >= 3 for running the core system and baseline
• Python 2.7 for running preprocessing scripts

• Operating Systems: macOS Mojave and Ubuntu 18.04
• Processor: Intel i5 Quad Core
• RAM: 8 GB DDR3

First run the script ./fetch_and_preprocess.sh

This downloads the following data:

• Stanford Sentiment Treebank (sentiment classification task)
• Glove word vectors (Common Crawl 840B) -- Warning: this is a 2GB download!

and the following libraries:

• Stanford Parser
• Stanford POS Tagger

Now to test the baseline model goto the baseline directory using cd ./baseline and run python3 baseline.py

For testing the implementation of Tree LSTM and Attention mechanism use the following command:

python3 sentiment.py --name <name_of_log_file> --model_name <constituency|dependency> --epochs 10 --attention_flag <True|False>

Important files:

- baseline.py: Contains baseline implementation of Linear LSTM
- sentiment.py: Main driver file to run the system. We have changed the argument processing and model generation and processing flow
- trainer.py: This file implements training module. We have added the functionality to incorporate the trainig of the model with and without the attention mechanism.
- model.py: This file contains implementation of all the models. We implemented attention module and changed the implementation of Tree LSTM modules to sync with our requirements.
- config.py: This file contains configuration constants to control the nature of system. We added extra configuration parameters to this to control our system.

1. Code for baseline has been referenced from https://github.com/adeshpande3/LSTM-Sentiment-Analysis
2. Code for Tree LSTM has been referenced from https://github.com/ttpro1995/TreeLSTMSentiment/

Apache