zensols.calamr 0.2.0
CALAMR: Component ALignment for AMR
Keywords: tooling

# CALAMR: Component ALignment for Abstract Meaning Representation

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This repository contains code for the paper [CALAMR: Component ALignment for
Abstract Meaning Representation] and aligns the components of a bipartite
source and summary AMR graph.  To reproduce the results of the paper, see the
[paper repository](https://github.com/uic-nlp-lab/calamr).

The results are useful as a semantic graph similarity score (like SMATCH) or to
find the summarized portion (as AMR nodes, edges and subgraphs) of a document
or the portion of the source that represents the summary.  If you use this
library or the [PropBank API/curated database], please [cite](#citation) our
paper.

Features:

* Align source/summary AMR graphs.
* Scores for extent to which AMRs are summarized or represented in their source
  text.
* Rendering of the alignments.
* Support for four AMR [corpora](#corpora).

<!-- markdown-toc start - Don't edit this section. Run M-x markdown-toc-refresh-toc -->
## Table of Contents

- [Documentation](#documentation)
- [Installing](#installing)
- [Corpora](#corpora)
- [Usage](#usage)
    - [Command Line](#command-line)
        - [Aligning Corpus Documents](#aligning-corpus-documents)
        - [Ad hoc Corpora](#ad-hoc-corpora)
    - [AMR Release 3.0 Corpus (LDC2020T02)](#amr-release-30-corpus-ldc2020t02)
    - [API](#api)
        - [Aligning Ad hoc Documents](#aligning-ad-hoc-documents)
        - [Aligning Corpora Documents](#aligning-corpora-documents)
    - [Docker](#docker)
- [Example Graphs](#example-graphs)
    - [GraphViz](#graphviz)
        - [The Nascent Graph (with flow data)](#the-nascent-graph-with-flow-data)
        - [The Source Graph](#the-source-graph)
    - [Plotly](#plotly)
- [Attribution](#attribution)
- [Citation](#citation)
- [Changelog](#changelog)
- [License](#license)

<!-- markdown-toc end -->



## Documentation

The recommended reading order for this project:

1. The [conference slides](https://plandes.github.io/calamr/doc/CalamrSlides.pdf)
1. The abstract and introduction of the paper [CALAMR: Component ALignment for
   Abstract Meaning Representation]
1. [Overview and implementation guide](https://plandes.github.io/calamr/doc/CalamrImplementGuide.pdf)
1. [Full documentation](https://plandes.github.io/calamr/index.html)
1. [API reference](https://plandes.github.io/calamr/api.html)


## Installing

The library can be installed with pip from the [pypi] repository:
```bash
pip3 install zensols.calamr
```

See [Installing the Gsii Model].


## Corpora

This repository contains code to support the following corpora with
source/summary AMR for alignment:

* [LDC2020T02] (AMR Release 3.0) Proxy Corpus
* [ISI] Little Prince
* [ISI] Bio AMR
* A [micro corpus] (toy corpus) used in the paper examples and [usage](#usage).


## Usage

The command-line tool and API does not depend on the repository.  However, it
has a template configuration file that both the CLI and the API use.  The
examples also use data in the repository.  Do the following to get started:

1. Clone this repository and change the working directory to it:
   ```bash
   git clone https://github.com/plandes/calamr && cd calamr
   ```
1. Copy the resource file:
   ```bash
   cp src/config/dot-calamrrc ~/.calamrrc
   ```


### Command Line

The steps below show how to use the command-line tool.  First set up the
application environment:

1. Edit the `~/.calamrrc` file to choose the corpus and visualization.  Keep
   the `calamr_corpus` set to `adhoc` for these examples.  (Note that you can
   also set the the `CALAMRRC` environment variable to a file in a different
   location if you prefer.)
1. Create the micro corpus:
   ```bash
   calamr mkadhoc --corpusfile corpus/micro/source.json
   ```
1. Print the document keys of the corpus:
   ```bash
   calamr keys
   ```


#### Aligning Corpus Documents

AMR corpora that distinguish between source and summary documents are needed so
the API knows what data to align.  The following examples utilize preexisting
corpora (including the last section's micro corpus):

1. Generate the Liu et al. graph for the micro corpus in directory `example`:
	```bash
	calamr aligncorp liu-example -f txt -o example
	```
1. Force the *Little Prince* AMR corpus download and confirm success with the
   single document key `1943`:
	```bash
	calamr keys --override=calamr_corpus.name=little-prince
	```
1. Use the default AMR parser to extract sentence text from the *Little Prince*
	AMR corpus using the [SPRING] (Bevilacqua et al. 2021) parser:
	```bash
	calamr penman -o lp.txt --limit 5 \
		--override amr_default.parse_model=spring \
		~/.cache/calamr/corpus/amr-rel/amr-bank-struct-v3.0.txt
	```
1. Score the parsed sentences using CALAMR, SMATCH and WLK:
	```bash
	calamr score --parsed lp.txt \
		--methods calamr,smatch,wlk \
		~/.cache/calamr/corpus/amr-rel/amr-bank-struct-v3.0.txt
	```


#### Ad hoc Corpora

The [micro corpus] can be edited and rebuilt to add your own data to be
aligned.  However, there's an easier way to align ad hoc documents.

1. Align a summarized document not included in any corpus.  First create the
   annotated documents as files `short-story.json`.
   ```json
   [
	   {
		   "id": "intro",
		   "body": "The Dow Jones Industrial Average and other major indexes pared losses.",
		   "summary": "Dow Jones and other major indexes reduced losses."
	   },
	   {
		   "id": "dow-stats",
		   "body": "The Dow ended 0.5% lower on Friday while the S&P 500 fell 0.7%. Among the S&P sectors, energy and utilities gained while technology and communication services lagged.",
		   "summary": "Dow sank 0.5%, S&P 500 lost 0.7% and energy, utilities up, tech, comms came down."
	   }
   ]
   ```
   Now align the documents using the `XFM Bart Base` AMR parser, rendering
   with the maximum number of steps (`-r 10`), and save results to `example`:
	```bash
	calamr align short-story.json --override amr_default.parse_model=xfm_bart_base -r 10 -o example -f txt
	```

The `-r` option controls how many intermediate graphs generated to show the
iteration of the algorithm over all the steps (see the paper for details).


### AMR Release 3.0 Corpus (LDC2020T02)

If you are using the AMR 3.0 corpus, there is a preprocessing step that needs
executing before it can be used.

The Proxy Report corpus from the AMR 3.0 does not have both the `alignments`
(text-to-graph alignments) and `snt-type` (indicates if a sentence is part of
the source or the summary) metadata.  By default, this API expects both.  To
merge them into one dataset do the following:

1. [Obtain or purchase](https://catalog.ldc.upenn.edu/LDC2020T02) the corpus.
1. Move the file where the software can find it:
   ```bash
   mkdir ~/.cache/calamr/download
   cp /path/to/amr_annotation_3.0_LDC2020T02.tgz ~/.cache/calamr/download
   ```
1. Merge the alignments and sentence descriptors:
   ```bash
   ./src/bin/merge-proxy-anons.py
   ```
1. Confirm the merge was successful by printing the document keys and align a report:
   ```bash
   calamr keys --override=calamr_corpus.name=proxy-report
   calamr aligncorp 20041010_0024 -f txt -o example \
	   --override calamr_corpus.name=proxy-report
   ```

### API

This section explains how to use the library's API directly in Python.


#### Aligning Ad hoc Documents

This is taken from the [ad hoc API example](./test/test-adhoc.py)

1. Get the resource bundle:
   ```python
   from zensols.amr import AmrSentence, AmrDocument, AmrFeatureDocument
   from zensols.calamr import DocumentGraph, FlowGraphResult, Resource, ApplicationFactory

   # get the resource bundle
   res: Resource = ApplicationFactory.get_resource()
   ```
1. Create test data:
   ```python
   # create AMR sentences
   test_summary = AmrSentence("""\
   # ::snt Joe's dog was chasing a cat in the garden.
   # ::snt-type summary
   # ::id liu-example.0
   (c / chase-01
	  :ARG0 (d / dog
			   :poss (p / person
						:name (n / name
								 :op1 "Joe")))
	  :ARG1 (c2 / cat)
	  :location (g / garden))""")
   test_body = AmrSentence("""\
   # ::snt I saw Joe's dog, which was running in the garden.
   # ::snt-type body
   # ::id liu-example.1
   (s / see-01
	  :ARG0 (ii / i)
	  :ARG1 (d / dog
			   :poss (p / person
						:name (n / name
								 :op1 "Joe"))
			   :ARG0-of (r / run-02
						   :location (g / garden))))""")

	# create the AMR document 
   adoc = AmrDocument((test_summary, test_body))
   ```
1. Create the annotated document and align it:
   ```python
   # convert the AMR document to an AMR annotated document with NLP features
   fdoc: AmrFeatureDocument = res.to_annotated_doc(adoc)
   # create the bipartite source/summary graph
   graph: DocumentGraph = res.create_graph(fdoc)
   # align the graph
   flow: FlowGraphResult = res.align(graph)
   ```
1. Get and visualize the results:
   ```python
   # write the summarization metrics
   flow.write()
   # render the results as a graph in a web browser
   flow.render()
   ```


#### Aligning Corpora Documents

To use an existing corpus (ad hoc "micro" corpus, The Little Prince, Biomedical
Corpus, or Proxy report 3.0), use the following API to speed things up:

1. Get the resource bundle:
   ```python
   from pathlib import Path
   from zensols.amr import AmrFeatureDocument
   from zensols.calamr import DocumentGraph, Resource, ApplicationFactory

   # get the resource bundle
   res: Resource = ApplicationFactory.get_resource()
   ```
1. Get the Liu et al. AMR feature document example and print it.
   ```python
   doc: AmrFeatureDocument = res.get_corpus_document('liu-example')
   doc.write()
   ```
   output:
   ```yaml
   [T]: Joe's dog was chasing a cat in the garden. I saw Joe's dog, which was running in the garden. The dog was chasing a cat.
   sentences:
       [N]: Joe's dog was chasing a cat in the garden.
           (c0 / chase-01~e.4
                 :location (g0 / garden~e.9)
                 :ARG0 (d0 / dog~e.2
                       :poss (p0 / person
                             :name (n0 / name
                                   :op1 "Joe"~e.0)))
                 :ARG1 (c1 / cat~e.6))
   .
   .
   .
   amr:
       summary:
           Joe's dog was chasing a cat in the garden.
       sections:
           no section sentences
               I saw Joe's dog, which was running in the garden.
               The dog was chasing a cat.
   ```
1. Align (if not already and cached) and get the flow results of the example:
   ```python
   flow = res.align_corpus_document('liu-example')
   flow.write()
   ```
   output:
   ```yaml
   summary:
       Joe's dog was chasing a cat in the garden.
   sections:
       no section sentences
           I saw Joe's dog, which was running in the garden.
           The dog was chasing a cat.
   statistics:
       agg:
           aligned_portion_hmean: 0.8695652173913044
           mean_flow: 0.7131309357900468
           tot_alignable: 21
           tot_aligned: 18
           aligned_portion: 0.8571428571428571
           reentrancies: 0
   ```
1. Parse the first document from the [ad hoc JSON file](#ad-hoc-corpora) align
   it, and give its statistics:
   ```python
   doc: AmrFeatureDocument = next(iter(res.parse_documents(Path('short-story.json'))))
   graph: DocumentGraph = res.create_graph(doc)
   flow = res.align(graph)
   flow.write()
   ```
   output:
   ```yaml
   summary:
       Dow Jones and other major indexes reduced losses.
   sections:
       no section sentences
           The Dow Jones Industrial Average and other major indexes pared losses.
   statistics:
       agg:
           aligned_portion_hmean: 1.0
           mean_flow: 0.9269955839429582
           tot_alignable: 24
           tot_aligned: 24
           aligned_portion: 1.0
           reentrancies: 0
    ...
	```
1. Render the results of a flow:
   ```python
   flow = res.align_corpus_document('liu-example')
   flow.render()
   ```
1. Render all graphs of the flow results of the flow to directory `example`:
   ```python
   flow.render(
       contexts=flow.get_render_contexts(include_nascent=True),
       directory=Path('example'),
       display=False)
   ```

### Docker

A stand-alone docker image is also available (see [CALAMR Docker
image](./docker)).  This [docker image] provides stand-alone container with all
models, configuration and the adhoc micro corpus installed.


## Example Graphs

The Liu et al. example graphs were created from the last step of the
[API](#api) examples, which is equivalent the first step of the [command line
example](#aligning-corpus-documents).


### GraphViz

To create these graphs, set your `~/.calamrrc` configuration to:

```ini
[calamr_default]
renderer = graphviz
```

#### The Nascent Graph (with flow data)

<p align="center">
	<img src="./doc/graphs/liu-nascent-graphviz.svg"
		alt="source graph" width="80%"
		style="outline: 5px solid #D3D3D3;"/>
</p>


#### The Source Graph

<p align="center">
	<img src="./doc/graphs/liu-source-graphviz.svg"
		alt="source graph" width="90%"
		style="outline: 5px solid #D3D3D3;"/>
</p>


### Plotly

To create these graphs, set your `~/.calamrrc` configuration to:

```ini
[calamr_default]
renderer = plotly
```

See the [interactive version](https://plandes.github.io/calamr/doc/graphs/liu-source-plotly.html).
[<img src="./doc/graphs/liu-source-plotly-screenshot.png">](https://plandes.github.io/calamr/doc/graphs/liu-source-plotly.html)


## Attribution

This project, or reference model code, uses:

* Python 3.11
* [amrlib] for AMR parsing.
* [amr_coref] for AMR co-reference
* [zensols.amr] for AMR features and summarization data structures.
* [Sentence-BERT] embeddings
* [zensols.propbankdb] and [zensols.deepnlp] for PropBank embeddings
* [zensols.nlparse] for natural language features and [NLP scoring]
* [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:

```bibtex
@inproceedings{landes-di-eugenio-2024-calamr-component,
    title = "{CALAMR}: Component {AL}ignment for {A}bstract {M}eaning {R}epresentation",
    author = "Landes, Paul  and
      Di Eugenio, Barbara",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italy",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.236",
    pages = "2622--2637"
}
```


## Changelog

An extensive changelog is available [here](CHANGELOG.md).


## License

[MIT License](LICENSE.md)

Copyright (c) 2023 - 2025 Paul Landes


<!-- links -->
[pypi]: https://pypi.org/project/zensols.calamr/
[pypi-link]: https://pypi.python.org/pypi/zensols.calamr
[pypi-badge]: https://img.shields.io/pypi/v/zensols.calamr.svg
[python310-badge]: https://img.shields.io/badge/python-3.10-blue.svg
[python310-link]: https://www.python.org/downloads/release/python-3100
[python311-badge]: https://img.shields.io/badge/python-3.11-blue.svg
[python311-link]: https://www.python.org/downloads/release/python-3110
[build-badge]: https://github.com/plandes/calamr/workflows/CI/badge.svg
[build-link]: https://github.com/plandes/calamr/actions

[micro corpus]: corpus/micro/source.json
[LDC2020T02]: https://catalog.ldc.upenn.edu/LDC2020T02
[SPRING]: https://github.com/SapienzaNLP/spring
[CALAMR: Component ALignment for Abstract Meaning Representation]: https://aclanthology.org/2024.lrec-main.236/
[ISI]: https://amr.isi.edu

[amrlib]: https://github.com/bjascob/amrlib
[amr_coref]: https://github.com/bjascob/amr_coref
[spaCy]: https://spacy.io
[Smatch]: https://github.com/snowblink14/smatch
[WLK]: https://github.com/flipz357/weisfeiler-leman-amr-metrics
[zensols.nlparse]: https://github.com/plandes/nlparse
[NLP scoring]: https://plandes.github.io/nlparse/api/zensols.nlp.html#zensols-nlp-score
[Sentence-BERT]: https://www.sbert.net
[docker image]: https://hub.docker.com/r/plandes/calamr
[zensols.amr]: https://github.com/plandes/amr
[zensols.deepnlp]: https://github.com/plandes/deepnlp
[zensols.propbankdb]: https://github.com/plandes/propbankdb
[PropBank API/curated database]: https://github.com/plandes/propbankdb
[Installing the Gsii Model]: https://github.com/plandes/amr#installing-the-gsii-model