Welcome to the exciting world of the HPSG Neural Parser, a Python implementation that brings advanced parsing techniques from the realm of Head-Driven Phrase Structure Grammar (HPSG) parsing to your fingertips. In this guide, we’ll walk step-by-step through how to set up, train, and evaluate the parser effectively.

1. Requirements

Before diving into parsing, let’s ensure you have everything you need:

• Python 3.6 or higher
• Cython 0.25.2 or any compatible version
• PyTorch 0.4.0 (Note: Not tested with PyTorch 1.0)
• EVALB – Compile by running ‘make’ in the EVALB directory
• AllenNLP 0.7.0 (needed for ELMo word representations)
• pytorch-transformers 1.0.0+ (needed for BERT and XLNet)

2. Pre-trained Models

Ready-to-use pre-trained parser models can be downloaded to kickstart your project:

• joint_cwt_best_dev=93.85_devuas=95.87_devlas=94.47.pt – Best English parser using Glove
• joint_bert_dev=95.55_devuas=96.67_devlas=94.86.pt – Best English parser using BERT
• joint_xlnet_dev=96.03_devuas=96.96_devlas=95.32.pt – Best English parser using XLNet

3. Training

Training your model comes next! First, download the PTB data files from here and place them in your data folder. The dependency structures are extracted from constituent structure using the Stanford Parser.

Imagine you are a chef preparing a master recipe. Each ingredient represents a specific piece of data required for the model to learn. You have your main ingredients (PTB dataset) and some optional spices (GloVe embeddings) that can elevate your dish to gourmet status. Following are the training instructions:

sh run_single.sh # to train a Joint-Span parser
sh run_bert.sh # to train a Joint-Span parser with BERT
sh run_xlnet.sh # to train a Joint-Span parser with XLNet

4. Evaluation Instructions

Evaluate your saved model on a test corpus using:

python src/main.py test --model-path-base  --evalb-dir  --test-ptb-path 

Following this command is akin to a quality check in your kitchen. You’re assessing whether your dish turned out as expected, based on the recipe and methods applied earlier.

Troubleshooting

If you encounter any issues during setup or execution, here are some ideas to help you troubleshoot:

• Ensure that all package versions are met – it’s common for mismatches to create conflicts.
• If encountering installation errors, check permissions for the directories where you are trying to install.
• To debug runtime issues, review the log files for any error messages or warnings.
• If the models do not seem to perform as expected, consider re-evaluating your training parameters.

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