zensols-amr 0.1.0

Adapts amrlib in the Zensols framework.

AMR annotation and feature generation

Provides support for AMR annotations and feature generation.

Features:

• Annotation in AMR metadata. For example, sentence types found in the Proxy report AMR corpus.
• AMR token alignment as spaCy components.
• A scoring API that includes Smatch and WLK, which extends a more general NLP scoring module.
• AMR parsing (amrlib) and AMR co-reference (amr_coref).
• Command line and API utilities for AMR graph Penman graphs, debugging and files.
• Tools for training and evaluating AMR parse models.

Documentation

• full documentation.
• API reference

Obtaining

The easiest way to install the command line program is via the pip installer:

pip3 install zensols.amr

Binaries are also available on pypi.

Usage

from penman.graph import Graph
from zensols.nlp import FeatureDocument, FeatureDocumentParser
from zensols.amr import AmrDocument, AmrSentence, ApplicationFactory

sent: str = """

He was George Washington and first president of the United States.
He was born On February 22, 1732.

""".replace('\n', ' ').strip()
# get the AMR document parser
doc_parser: FeatureDocumentParser = ApplicationFactory.get_doc_parser()
# the parser creates a NLP centric feature document as provided in the
# zensols.nlp package
doc: FeatureDocument = doc_parser(sent)
# the AMR object graph data structure is provided in the feature document
amr_doc: AmrDocument = doc.amr
# dump a human readable output of the AMR document
amr_doc.write()
# get the first AMR sentence instance
amr_sent: AmrSentence = amr_doc.sents[0]
print('sentence:')
print(' ', amr_sent.text)
print('tuples:')
# show the Penman graph representation
pgraph: Graph = amr_sent.graph
print(f'variables: {", ".join(pgraph.variables())}')
for t in pgraph.triples:
	print(' ', t)
print('edges:')
for e in pgraph.edges():
	print(' ', e)

Per the example, the t5.conf and gsii.conf configuration show how to include configuration needed per AMR model. These files can also be used directly with the amr command using the --config option.

However, the other resources in the example must be imported unless you redefine them yourself.

Library

When adding the amr spaCy pipeline component, the doc._.amr attribute is set on the Doc instance. You can either configure spaCy yourself, or you can use the configuration files in test-resources as an example using the zensols.util configuration framework. The command line application provides an example how to do this, along with the test case.

Command Line

This library is written mostly to be used by other program, but the command line utility amr is also available to demonstrate its usage and to generate ARM graphs on the command line.

To parse:

$ amr parse -c test-resources/t5.conf 'This is a test of the AMR command line utility.'
# ::snt This is a test of the AMR command line utility.
(t / test-01
   :ARG1 (u / utility
			:mod (c / command-line)
			:name (n / name
					 :op1 "AMR"
					 :toki1 "6")
			:toki1 "9")
   :domain (t2 / this
			   :toki1 "0")
   :toki1 "3")

To generate graphs in PDF format:

$ amr plot -c test-resources/t5.conf 'This is a test of the AMR command line utility.'
wrote: amr-graph/this-is-a-test-of-the-amr-comm.pdf

Performance (Smatch)

This repo is configured to download and train on the AMR bio-medical corpus. The results of the scores using amrlib's default smatch score is:

Corpus	Model	Precision	Recall	F-score
bio	amrlib t5	0.5613647022821542	0.4799029769470724	0.5174473330001563
bio	amrlib t5 + bio	0.6792187759112143	0.6164372669678633	0.6463069704295633

Attribution

This project, or reference model code, uses:

• Python 3
• spaCy for natural language parsing.
• zensols.nlparse for natural language features.
• amrlib for AMR parsing.
• amr_coref for AMR co-reference
• Smatch (Cai and Knight. 2013) and WLK (Opitz et. al. 2021) for scoring.

Citation

If you use this project in your research please use the following BibTeX entry:

@inproceedings{landes-etal-2023-deepzensols,
	title = "{D}eep{Z}ensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility",
	author = "Landes, Paul  and
	  Di Eugenio, Barbara  and
	  Caragea, Cornelia",
	editor = "Tan, Liling  and
	  Milajevs, Dmitrijs  and
	  Chauhan, Geeticka  and
	  Gwinnup, Jeremy  and
	  Rippeth, Elijah",
	booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
	month = dec,
	year = "2023",
	address = "Singapore, Singapore",
	publisher = "Empirical Methods in Natural Language Processing",
	url = "https://aclanthology.org/2023.nlposs-1.16",
	pages = "141--146"
}

Changelog

An extensive changelog is available here.

Community

Please star this repository and let me know how and where you use this API. Contributions as pull requests, feedback and any input is welcome.

License

MIT License

Copyright (c) 2021 - 2023 Paul Landes