Skip to main content

Libraries and scripts for DELPH-IN data

Project description

PyDelphin — Python libraries for DELPH-IN data

PyPI Version Python Support Test Status Documentation Status Discourse topics

DELPH-IN is an international consortium of researchers committed to producing precise, high-quality language processing tools and resources, primarily in the HPSG syntactic and MRS semantic frameworks, and PyDelphin is a suite of Python libraries for processing data and interacting with tools in the DELPH-IN ecosystem. PyDelphin's goal is to lower the barriers to making use of DELPH-IN resources to help users quickly build applications or perform experiments, and it has been successfully used for research into machine translation (e.g., Goodman, 2018), sentence chunking (Muszyńska, 2016), neural semantic parsing (Buys & Blunsom, 2017), natural language generation (Hajdik et al., 2019), and more.

Documentation, including guides and an API reference, is available here: http://pydelphin.readthedocs.io/

New to PyDelphin? Want to see examples? Try the walkthrough.

Installation and Upgrading

Get the latest release of PyDelphin from PyPI:

$ pip install pydelphin

For more information about requirements, installing from source, and running unit tests, please see the documentation.

API changes in new versions are documented in the CHANGELOG, but for any unexpected changes please file an issue.

Features

PyDelphin contains the following modules:

Semantic Representations:

Semantic Components and Interfaces:

Grammar and Parse Inspection:

Tokenization:

Corpus Management and Processing:

Interfaces with External Processors:

Core Components and Command Line Interface:

Other Information

Getting Help

Please use the issue tracker for bug reports, feature requests, and documentation requests. For more general questions and support, try one of the following channels maintained by the DELPH-IN community:

Citation

Please use the following for academic citations (and see: https://ieeexplore.ieee.org/abstract/document/8939628):

@INPROCEEDINGS{Goodman:2019,
  author={Goodman, Michael Wayne},
  title={A Python Library for Deep Linguistic Resources},
  booktitle={2019 Pacific Neighborhood Consortium Annual Conference and Joint Meetings (PNC)},
  year={2019},
  month=oct,
  address={Singapore},
  keywords={research software;linguistics;semantics;HPSG;computational linguistics;natural language processing;open source software}
}

Acknowledgments

Thanks to PyDelphin's contributors and all who've participated by filing issues and feature requests. Also thanks to the users of PyDelphin!

Related Software

Spelling

Earlier versions of PyDelphin were spelled "pyDelphin" with a lower-case "p" and this form is used in several publications. The current recommended spelling has an upper-case "P".

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

PyDelphin-1.7.0.tar.gz (154.1 kB view details)

Uploaded Source

Built Distribution

PyDelphin-1.7.0-py3-none-any.whl (184.8 kB view details)

Uploaded Python 3

File details

Details for the file PyDelphin-1.7.0.tar.gz.

File metadata

  • Download URL: PyDelphin-1.7.0.tar.gz
  • Upload date:
  • Size: 154.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.7

File hashes

Hashes for PyDelphin-1.7.0.tar.gz
Algorithm Hash digest
SHA256 504624cecdb64157746ac351e24094cf0016762aeb70d92d67003557e7f444c4
MD5 39485c92e29075fba221ec88f1a7c75b
BLAKE2b-256 dfc44b44070a4487aae8831c31c82e9c51667e00c9cd9913509a57aecdd727d2

See more details on using hashes here.

File details

Details for the file PyDelphin-1.7.0-py3-none-any.whl.

File metadata

  • Download URL: PyDelphin-1.7.0-py3-none-any.whl
  • Upload date:
  • Size: 184.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.7

File hashes

Hashes for PyDelphin-1.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 d5f49be043b49a6d16f3a34d2c6c86dfe2b17bbf853d17f129e8340514700737
MD5 629f6486d161a372b97fcd79b18009d2
BLAKE2b-256 0fe979767d9b650f72f08174c2a57f86d40c6e5bf6b3df3d0de445ced40d3cf7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page