spacyr provides an interface between R and spaCy, a natural language processing library with capabilities for linguistic annotation and information extraction. It enables researchers to apply spaCy’s text-processing functions within R-based analysis workflows.
The package communicates with spaCy through reticulate, which provides interoperability between R and Python. This makes it possible to process text using spaCy’s linguistic models and then investigate the annotated results using other R packages.
spacyr is particularly useful when working with large collections of documents requiring grammatical information, named entity recognition or syntactic analysis. It also integrates with quanteda, providing a route from linguistic annotation to quantitative text analysis.
This is free and open source software.
Key Features
- Tokenises text using spaCy’s linguistic processing pipeline.
- Segments documents into sentences and individual tokens.
- Provides part-of-speech tagging with universal and detailed grammatical tagsets.
- Extracts word lemmas for normalising inflected forms.
- Performs dependency parsing to identify relationships between words.
- Supports named entity recognition for identifying people, organisations, places and other entities.
- Extracts noun phrases from annotated documents.
- Provides functions for consolidating identified entities and noun phrases.
- Returns structured token annotations suitable for further statistical analysis.
- Supports language models available through the underlying spaCy installation.
- Offers optional parallel processing for applicable annotation operations.
- Accepts character vectors and compatible corpus objects as input.
- Provides an interface for initialising and managing spaCy processing environments.
Website: github.com/quanteda/spacyr
Support:
Developer: Kenneth Benoit and contributors
License: GNU General Public License v3.0
spacyr is written in R. Learn R with our recommended free books and free tutorials.
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|---|---|
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