Typed Dependency Tree LSTM in Pytorch

This is a PyTorch implementation of Typed Dependency Tree-LSTM as published in the paper An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies by Jeena Kleenankandy and Dr. K. A. Abdul Nazeer.

Requirements

Python (tested on 3.6.5, should work on >=2.7)
Java >= 8 (for Stanford CoreNLP utilities)
Other dependencies are in requirements.txt 

Contents :

• fetch_and_preprocess.sh :
  • downloads the SICK dataset, Stanford Parser and POS Tagger, and Glove word vectors (Common Crawl 840)
  • generate the dependency parses using Stanford Neural Network Dependency Parser.
• main.py : does training and testing of the model.
• config.py: list of all command-line arguments and their default values
• TD_set.py : list of universal dependencies used (update this list if you are using a different version)

Usage

To run the code execute these steps :

- bash fetch_and_preprocess.sh
- pip install -r requirements.txt
- python main.py

Acknowledgements

Shout-out to Riddhiman Dasgupta for Pytorch implementation of the paper Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks, which served as a starter code for this project.