DocuScope

docuscospacy: Support for spaCy models trained on DocuScope and the CLAWS7 tagset

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The docuscospacy package contains a set of functions to facilitate the processing of tagged corpora using:

The current version of the package runs in polars

The package can also convert a corpus to and from:

  • tmtoolkit – a set of tools for text mining and topic modeling

The documentation for docuscospacy is available on docuscospacy.readthedocs.org and the GitHub code repository is on github.com/browndw/docuscospacy.

Requirements and installation

docuscospacy works with Python 3.10 or newer (tested up to Python 3.12). It also requires spacy >= 3.8.

The recommended way of installing docuscospacy is to:

pip install docuscospacy

The optional dtm_to_coo helper returns a SciPy sparse matrix for interoperability with tools such as tmtoolkit. Install the sparse extra if you need that conversion:

pip install "docuscospacy[sparse]"

Note that installing the model depends on your spaCy version. Some versions allow:

pip install https://huggingface.co/browndw/en_docusco_spacy/resolve/main/en_docusco_spacy-1.5-py3-none-any.whl

But new ones may require:

pip install "en_docusco_spacy @ https://huggingface.co/browndw/en_docusco_spacy/resolve/main/en_docusco_spacy-1.5-py3-none-any.whl"

Features

Corpus analysis

The docuscospacy package supports the post-tagging generation of:

Outputs can be controlled either by part-of-speech or by DocuScope tag. Thus, can as noun and can as verb, for example, can be disambiguated.

Additionally, tagged multi-token sequences are aggregated for analysis. So, for example, where in spite of is tagged as a token sequence, it is combined into a single token.

Other features

  • KWIC tables that locate a node word in a center column with context columns on either side

Limits

  • the model that this package is designed for has only been trained on English

  • all data must reside in memory, i.e. no streaming of large data from the hard disk (which for example Gensim supports)

License

Code licensed under Apache License 2.0. See LICENSE file.

Project links

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Credits

Author: David Brown

GitHub Statistics

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License

Apache Software License (Apache License 2.0)

Requires

Python >=3.10

Provides Extra

sparse

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