How to Get Dependency Parse Output from SyntaxNet

How to Get Dependency Parse Output from SyntaxNet

Introduction

SyntaxNet, a neural network-based parser developed by Google, provides a powerful tool for extracting dependency relationships from text. This article guides you through obtaining dependency parse output from SyntaxNet.

Installation and Setup

1. Install the necessary dependencies, including TensorFlow, Python 2.7, and Bazel. Refer to the official SyntaxNet documentation for detailed instructions.
2. Download the SyntaxNet source code from the GitHub repository.
3. Build the SyntaxNet parser using Bazel.

Running the Parser

1. Navigate to the SyntaxNet directory in your terminal.
2. Use the following command to run the parser:

 bazel-bin/syntaxnet/parser_eval \ --input=path/to/input.txt \ --output=path/to/output.txt \ --model=path/to/model.pb \ --hparams=path/to/hparams.proto 

• Replace path/to/input.txt with the path to your input text file.
• Replace path/to/output.txt with the desired output file path.
• Replace path/to/model.pb with the path to the trained model file.
• Replace path/to/hparams.proto with the path to the hyperparameter configuration file.

Output Format

The dependency parse output is stored in the specified output file in a tabular format. Each line represents a dependency relation, with the following columns:

Example

Input Text

 The quick brown fox jumps over the lazy dog. 

Output

 1 The 0 ROOT ROOT 2 quick 1 AMOD amod 3 brown 2 AMOD amod 4 fox 3 NOUNMOD nmod 5 jumps 4 VERB nsubj 6 over 5 ADP prep 7 the 6 DET det 8 lazy 7 AMOD amod 9 dog 8 NOUNMOD nmod 

Conclusion

This article provided a step-by-step guide on obtaining dependency parse output from SyntaxNet. By following these instructions, you can leverage the power of neural network parsing to extract valuable syntactic information from your text data.