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Welcome to the am-parser wiki!

This documentation is a work in progress. If anything is unclear, please open an issue and we will get back to you as quickly as possible.

A characteristic feature of the AM parser is that it learns to parse sentences into AM dependency trees, which then evaluate to graphs in the AM algebra. We have defined our own file format, AM-CoNLL, to store AM dependency trees; it is a mild extension of the well-known CoNLL format for storing dependency trees.

Internal note: There is an UdS specific documentation on how to set up the environment and where to find the files.

The AM parser pipeline

Training the AM parser consists of two steps:

1. Preprocessing and decomposition: convert the graphbank to AM dependency trees
2. Training the neural network to predict AM dependency trees
3. Use the model to parse the test data.
4. Evaluation on graphs or AM dependency trees is described here.

Other ways of running the AM parser

You can use our pretrained models to directly parse sentences into graphs (this is also explained in the main README).

Alternatively, you can just let the am-parser compute the scores, and use some other parser to produce trees in the AM-CoNLL format, e.g. the A* parser. In this case, you will need to convert the AM-CoNLL trees to graphs yourself.

Utilities

• There are multiple options to visualize AM dependency trees (by sentence or for the whole corpus) for further error analysis.
• You can visualize graphbanks, creating a pdf file for every graph in the graph bank.
• UCCA preprocessing