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

• en_docusco_spacy -- a spaCy model trained on the CLAWS7 tagset and DocuScope; and
• 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.

docuscospacy works with Python 3.8 or newer (tested up to Python 3.10). It also requires spacy >= 3.3.

The recommended way of installing docuscospacy is to:

• create and activate a Python Virtual Environment ("venv")
• install spacy and tmtoolkit with a recommended set of dependencies
• download the en_docusco_spacy model
• install docuscospacy

pip install docuscospacy

The docuscospacy package supports the post-tagging generation of:

• Tagged token frequency tables
• Tag frequency tables
• Ngram/ntag tables
• Collocation tables around a node word and tag
• Document term matrices for tags
• Keyword comparisons against a reference corpus

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.

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

• 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)

Code licensed under Apache License 2.0. See LICENSE file.