zensols-nlp 1.13.1
This framework wraps the spaCy framework and creates light weight features in a class hierarchy that reflects the structure of natural language
License: MIT
Requires Python: <3.15,>=3.11
Keywords: nlp, spacy, tooling

# Zensols Natural Language Parsing

[![PyPI][pypi-badge]][pypi-link]
[![Python 3.13][python313-badge]][python313-link]
[![Python 3.12][python312-badge]][python312-link]
[![Build Status][build-badge]][build-link]

From the paper [DeepZensols: A Deep Learning Natural Language Processing
Framework for Experimentation and Reproducibility].  This framework wraps the
[spaCy] framework and creates light weight features in a class [hierarchy] that
reflects the structure of natural language.  The motivation is to generate
features from the parsed text in an object oriented fashion that is fast and
easy to pickle.

Other features include:
* [Parse and normalize] a stream of tokens as stop words, punctuation
  filters, up/down casing, porter stemming and [others].
* [Detached features] that are safe and easy to pickle to disk.
* Configuration drive parsing and token normalization using [configuration
  factories].
* Pretty print functionality for easy natural language feature selection.
* A comprehensive [scoring module] including following scoring methods:
  * [Rouge]
  * [Bleu]
  * [SemEval-2013 Task 9.1]
  * [Levenshtein distance]
  * Exact match

## Documentation

* [Full documentation]
* [Natural Language Parsing]
* [List Token Normalizers and Mappers]


## Obtaining / Installing

The library can be installed with pip from the [pypi] repository:
```bash
pip3 install zensols.nlp
```
The smallest base spaCy model will automatically be downloaded on the first
use.  You can download other models, such as the medium base model using the
following command:
```bash
python -m spacy download en_core_web_md
```


## Usage

A parser using the default configuration can be obtained by:
```python
from zensols.nlp import FeatureDocumentParser
parser: FeatureDocumentParser = FeatureDocumentParser.default_instance()
doc = parser('Obama was the 44th president of the United States.')
for tok in doc.tokens:
    print(tok.norm, tok.pos_, tok.tag_)
print(doc.entities)
```
output:
```
Obama PROPN NNP
was AUX VBD
the DET DT
45th ADJ JJ
president NOUN NN
of ADP IN
the United States DET DT
. PUNCT .
(<Obama>, <45th>, <the United States>)
```

However, minimal effort is needed to configure the parser using a [resource library]:
```python
from io import StringIO
from zensols.config import ImportIniConfig, ImportConfigFactory
from zensols.nlp import FeatureDocument, FeatureDocumentParser

CONFIG = """
# import the `zensols.nlp` library
[import]
config_file = resource(zensols.nlp): resources/obj.conf

# override the parse to keep only the norm, ent
[doc_parser]
token_feature_ids = set: ent_, tag_
"""

if (__name__ == '__main__'):
    fac = ImportConfigFactory(ImportIniConfig(StringIO(CONFIG)))
    doc_parser: FeatureDocumentParser = fac('doc_parser')
    sent = 'He was George Washington and first president of the United States.'
    doc: FeatureDocument = doc_parser(sent)
    for tok in doc.tokens:
        tok.write()
```

This uses a [resource library] to source in the configuration from this package
so minimal configuration is necessary.  More advanced configuration [examples]
are also available.

See the [feature documents] for more information.


## Scoring

Certain scores in the [scoring module] need additional Python packages.  These
are installed with:
```bash
pip install -R src/python/requirements-score.txt
```


## Attribution

This project, or example code, uses:
* [spaCy] for natural language parsing
* [msgpack] and [smart-open] for Python disk serialization
* [nltk] for the [porter stemmer] functionality


## Citation

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

```bibtex
@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 = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.nlposs-1.16",
    pages = "141--146"
}
```


## Changelog

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


## Community

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


## License

[MIT License](LICENSE.md)

Copyright (c) 2020 - 2026 Paul Landes


<!-- links -->
[pypi]: https://pypi.org/project/zensols.nlp/
[pypi-link]: https://pypi.python.org/pypi/zensols.nlp
[pypi-badge]: https://img.shields.io/pypi/v/zensols.nlp.svg
[python313-badge]: https://img.shields.io/badge/python-3.13-blue.svg
[python313-link]: https://www.python.org/downloads/release/python-3130
[python312-badge]: https://img.shields.io/badge/python-3.12-blue.svg
[python312-link]: https://www.python.org/downloads/release/python-3120
[build-badge]: https://github.com/plandes/nlparse/workflows/CI/badge.svg
[build-link]: https://github.com/plandes/nlparse/actions

[DeepZensols: A Deep Learning Natural Language Processing Framework for Experimentation and Reproducibility]: https://aclanthology.org/2023.nlposs-1.16.pdf
[examples]: https://github.com/plandes/nlparse/tree/master/example/config

[hierarchy]: https://plandes.github.io/nlparse/api/zensols.nlp.html#zensols.nlp.container.FeatureDocument
[Parse and normalize]: https://plandes.github.io/nlparse/doc/parse.html
[others]: https://plandes.github.io/nlparse/doc/normalizers.html
[Detached features]: https://plandes.github.io/nlparse/doc/parse.html#detached-features
[full documentation]: https://plandes.github.io/nlparse/
[Full documentation]: https://plandes.github.io/nlparse/api.html
[Natural Language Parsing]: https://plandes.github.io/nlparse/doc/parse.html
[List Token Normalizers and Mappers]: https://plandes.github.io/nlparse/doc/normalizers.html
[resource library]: https://plandes.github.io/util/doc/config.html#resource-libraries

[spaCy]: https://spacy.io
[nltk]: https://www.nltk.org
[smart-open]: https://pypi.org/project/smart-open/
[msgpack]: https://msgpack.org
[porter stemmer]: https://tartarus.org/martin/PorterStemmer/

[configuration factories]: https://plandes.github.io/util/doc/config.html#configuration-factory
[feature documents]: https://plandes.github.io/nlparse/doc/feature-doc.html
[scoring module]: https://plandes.github.io/nlparse/api/zensols.nlp.html#zensols-nlp-score
[Rouge]: https://aclanthology.org/W04-1013
[Bleu]: https://aclanthology.org/P02-1040
[SemEval-2013 Task 9.1]: https://web.archive.org/web/20150131105418/https://www.cs.york.ac.uk/semeval-2013/task9/data/uploads/semeval_2013-task-9_1-evaluation-metrics.pdf
[Levenshtein distance]: https://en.wikipedia.org/wiki/Levenshtein_distance