The Constituency Tree Labeling

Overview

This is an implementation of an LSTM + CRF model that predicts a tag for each node in the constituency tree. It was completed as part of the 10-618 course at CMU. We assume that for each example, the branching structure of the tree is known, but the tags are not. The model is trained on a Penn Tree Bank dataset.

• Input: An input sentence and the associated skeleton of its constituency parse tree
• Output: The labels of the non terminals in the parse tree

Usage

1. Clone and navigate to the repository:

   git clone https://github.com/alako/constituency-tree.git
   cd constituency-tree
   

2. Create a Virtual Environment with venv or a Conda Environment:

   conda create --name myenv python=3.10 pip
   conda activate myenv
   

3. Run the following command to install dependencies from requirements.txt:

   pip install -r requirements.txt
   

4. Start the training:

   python tree_bp.py