Zensols Natural Language Parsing

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.

• See the full documentation.
• Paper on arXiv.

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.

Documentation

• Framework documentation
• Natural Language Parsing
• List Token Normalizers and Mappers

Usage

An example that provides ways to configure the parser is given here. See the makefile or ./run.py -h for command line usage.

A very simple example is given below:

from io import StringIO
from zensols.config import ImportIniConfig, ImportConfigFactory
from zensols.nlp import FeatureDocument, FeatureDocumentParser

CONFIG = """
[import]
sections = list: imp_conf

# import the `zensols.nlp` library
[imp_conf]
type = importini
config_files = list: 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.

See the feature documents for more information.

Obtaining / Installing

1. The easist way to install the command line program is via the pip installer: pip3 install zensols.nlp
2. Install at least one spaCy model: python -m spacy download en_core_web_sm

Binaries are also available on pypi.

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:

@article{Landes_DiEugenio_Caragea_2021,
  title={DeepZensols: Deep Natural Language Processing Framework},
  url={http://arxiv.org/abs/2109.03383},
  note={arXiv: 2109.03383},
  journal={arXiv:2109.03383 [cs]},
  author={Landes, Paul and Di Eugenio, Barbara and Caragea, Cornelia},
  year={2021},
  month={Sep}
}

Changelog

An extensive changelog is available here.

License

MIT License

Copyright (c) 2020 - 2021 Paul Landes