amr-evaluation-enhanced (this is a variant of https://github.com/mdtux89/amr-evaluation)

Evaluation metrics to compare AMR graphs based on Smatch (http://amr.isi.edu/evaluation.html). The script computes a set of metrics between AMR graphs in addition to the traditional Smatch code:

• Unlabeled(differ): Smatch score computed on the predicted graphs after (canonicalizing direction and) removing all edge labels
• No WSD. Smatch score while ignoring Propbank senses (e.g., duck-01 vs duck-02)
• Named Ent. F-score on the named entity recognition (:name roles)
• Non_sense_frames(new). F-score on Propbank frame identification without sense (e.g. duck-00)
• Frames(new). F-score on Propbank frame identification without sense (e.g. duck-01)
• Wikification. F-score on the wikification (:wiki roles)
• Negations. F-score on the negation detection (:polarity roles)
• Concepts. F-score on the concept identification task
• Reentrancy. Smatch computed on reentrant edges only
• SRL. Smatch computed on :ARG-i roles only

The different metrics were introduced in the paper below, which also uses them to evaluate several AMR parsers:

"An Incremental Parser for Abstract Meaning Representation", Marco Damonte, Shay B. Cohen and Giorgio Satta. Proceedings of EACL (2017). URL: https://arxiv.org/abs/1608.06111

(Some of the metrics were recently fixed and updated)

Usage: ./evaluation.sh <parsed data> <gold data>, where and are two files which contain multiple AMRs. A blank line is used to separate two AMRs (same format required by Smatch).

In the paper we also discuss a metric for noun phrase analysis. To compute this metric:

• ./preprocessing.sh <gold data> and python extract_np.py <gold data> to extract the noun phrases from your gold dataset. This will create two files: np_sents.txt and np_graphs.txt.
• Parse np_sents.txt with the AMR parser and evaluate with Smatch python smatch/smatch.py --pr -f <parsed data> np_graphs.txt