| Name | pybiber JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Extract Biber features from a document parsed and annotated by spaCy. |
| upload_time | 2025-09-08 16:40:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License
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pybiber: A comprehensive Python package for linguistic feature extraction and Multi-Dimensional Analysis
========================================================================================================
|pypi| |pypi_downloads| |tests|
The pybiber package provides tools for extracting 67 lexicogrammatical and functional features described by `Biber (1988) <https://books.google.com/books?id=CVTPaSSYEroC&dq=variation+across+speech+and+writing&lr=&source=gbs_navlinks_s>`_ and widely used for text-type, register, and genre classification tasks in corpus linguistics.
**Key Features:**
- **67 Linguistic Features**: Automated extraction of tense markers, pronouns, subordination patterns, modal verbs, and more
- **Multi-Dimensional Analysis**: Complete implementation of Biber's MDA methodology for register analysis
- **Principal Component Analysis**: Alternative dimensionality reduction approaches with visualization tools
- **High Performance**: Built on `spaCy <https://spacy.io/models>`_ and `Polars <https://docs.pola.rs/>`_ for efficient text processing
- **End-to-End Pipeline**: From raw text files to statistical analysis in just a few lines of code
- **Comprehensive Visualization**: Built-in plotting functions for exploratory data analysis
**Applications:**
- Register and genre analysis in corpus linguistics
- Text classification and machine learning preprocessing
- Diachronic language change studies
- Cross-linguistic variation research
- Academic writing analysis and pedagogical applications
- Stylometric analysis and authorship attribution
The package uses `spaCy <https://spacy.io/models>`_ part-of-speech tagging and dependency parsing with `Polars <https://docs.pola.rs/>`_ DataFrames for high-performance analytics.
**Accuracy Note**: Feature extraction builds from probabilistic taggers, so accuracy depends on model quality. Texts with irregular spellings or non-standard punctuation may produce unreliable outputs unless taggers are specifically tuned for those domains.
See `the documentation <https://browndw.github.io/pybiber>`_ for comprehensive guides and API reference.
See `pseudobibeR <https://cran.r-project.org/web/packages/pseudobibeR/index.html>`_ for the R implementation.
Quick Start
-----------
**One-line processing** from a folder of text files:
.. code-block:: python
import pybiber as pb
# Process all .txt files in a directory
pipeline = pb.PybiberPipeline(model="en_core_web_sm")
features = pipeline.run_from_folder("path/to/texts")
**Multi-Dimensional Analysis** with visualization:
.. code-block:: python
# Create analyzer for statistical analysis
analyzer = pb.BiberAnalyzer(features)
# Perform MDA and generate scree plot
mda_results = analyzer.mda()
analyzer.mdaviz_screeplot()
# Plot group means by dimension
analyzer.mdaviz_groupmeans(grouping_var="register")
Installation
------------
You can install the released version of pybiber from `PyPI <https://pypi.org/project/pybiber/>`_:
.. code-block:: install-pybiber
pip install pybiber
Install a `spaCY model <https://spacy.io/usage/models#download>`_:
.. code-block:: install-model
python -m spacy download en_core_web_sm
Usage
-----
**Data Requirements**
The pybiber package works with corpora structured as DataFrames with:
- ``doc_id`` column: Unique document identifiers
- ``text`` column: Raw text content
This follows conventions from `readtext <https://readtext.quanteda.io/articles/readtext_vignette.html>`_ and `quanteda <https://quanteda.io/>`_.
**Step-by-Step Workflow**
1. **Import libraries and load spaCy model**:
.. code-block:: python
import spacy
import pybiber as pb
from pybiber.data import micusp_mini # Sample corpus
nlp = spacy.load("en_core_web_sm")
2. **Parse corpus with spaCy**:
.. code-block:: python
# Parse texts to extract linguistic annotations (modern approach)
processor = pb.CorpusProcessor()
tokens_df = processor.process_corpus(micusp_mini, nlp)
3. **Extract Biber features**:
.. code-block:: python
# Aggregate 67 linguistic features per document
features_df = pb.biber(tokens_df)
4. **Advanced Analysis** (optional):
.. code-block:: python
# Statistical analysis and visualization
analyzer = pb.BiberAnalyzer(features_df)
# Multi-Dimensional Analysis
mda_results = analyzer.mda()
# Principal Component Analysis
pca_results = analyzer.pca()
# Visualization options
analyzer.mdaviz_screeplot() # Eigenvalue plot
analyzer.pcaviz_contrib() # Feature contributions
analyzer.mdaviz_groupmeans(group_var="genre") # Group comparisons
**Pipeline Convenience Functions**
For streamlined processing, use the high-level pipeline:
.. code-block:: python
from pybiber import PybiberPipeline
pipeline = PybiberPipeline(model="en_core_web_sm", disable_ner=True)
# From folder of .txt files
features_df = pipeline.run_from_folder("/path/to/texts")
# From in-memory corpus
features_df, tokens_df = pipeline.run(corpus_df, return_tokens=True)
# One-liner convenience functions
features_df = pb.run_biber_from_folder("/path/to/texts")
features_df = pb.run_biber(corpus_df)
Feature Categories
------------------
The package extracts 67 linguistic features across 16 categories:
- **Tense & Aspect**: Past tense, perfect aspect, present tense
- **Adverbials**: Place and time adverbials
- **Pronouns**: 1st/2nd/3rd person, demonstrative, indefinite pronouns
- **Questions**: Direct wh-questions
- **Nominal Forms**: Nominalizations, gerunds, nouns
- **Passives**: Agentless and by-passives
- **Stative Forms**: *be* as main verb, existential *there*
- **Subordination**: 18 different clause types (that-clauses, wh-clauses, infinitives, relatives, etc.)
- **Modification**: Prepositional phrases, attributive/predicative adjectives, adverbs
- **Lexical Specificity**: Type-token ratio, word length
- **Lexical Classes**: Conjuncts, hedges, amplifiers, emphatics, discourse particles
- **Modals**: Possibility, necessity, and predictive modals
- **Specialized Verbs**: Public, private, suasive verbs
- **Reduced Forms**: Contractions, deletions, split constructions
- **Coordination**: Phrasal and clausal coordination
- **Negation**: Synthetic and analytic negation
See the `full feature list <https://browndw.github.io/pybiber/feature-categories.html>`_ for detailed descriptions.
Performance & Requirements
--------------------------
**System Requirements:**
- Python 3.10+
- spaCy model with POS tagging and dependency parsing (e.g., ``en_core_web_sm``)
**Performance Notes:**
- Built on Polars for fast DataFrame operations
- Supports multiprocessing for large corpora
- Memory-efficient processing with configurable batch sizes
- Processing time: ~20-30 seconds for small corpora (e.g., 500 documents)
License
-------
Code licensed under the `MIT License <https://opensource.org/license/mit/>`_.
See the `LICENSE <https://github.com/browndw/pybiber/blob/master/LICENSE>`_ file.
.. |pypi| image:: https://badge.fury.io/py/pybiber.svg
:target: https://badge.fury.io/py/pybiber
:alt: PyPI Version
.. |pypi_downloads| image:: https://img.shields.io/pypi/dm/pybiber
:target: https://pypi.org/project/pybiber/
:alt: Downloads from PyPI
.. |tests| image:: https://github.com/browndw/pybiber/actions/workflows/test.yml/badge.svg
:target: https://github.com/browndw/pybiber/actions/workflows/test.yml
:alt: Test Status
Raw data
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"name": "pybiber",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "David Brown <dwb2@andrew.cmu.edu>",
"keywords": "nlp, language",
"author": null,
"author_email": "David Brown <dwb2@andrew.cmu.edu>",
"download_url": "https://files.pythonhosted.org/packages/83/1b/694338e1245e6c3f8f5d6f8aa4878c88e47b95420b4768a24d1592a6a90f/pybiber-0.2.0.tar.gz",
"platform": null,
"description": "\npybiber: A comprehensive Python package for linguistic feature extraction and Multi-Dimensional Analysis\n========================================================================================================\n|pypi| |pypi_downloads| |tests|\n\nThe pybiber package provides tools for extracting 67 lexicogrammatical and functional features described by `Biber (1988) <https://books.google.com/books?id=CVTPaSSYEroC&dq=variation+across+speech+and+writing&lr=&source=gbs_navlinks_s>`_ and widely used for text-type, register, and genre classification tasks in corpus linguistics.\n\n**Key Features:**\n\n- **67 Linguistic Features**: Automated extraction of tense markers, pronouns, subordination patterns, modal verbs, and more\n- **Multi-Dimensional Analysis**: Complete implementation of Biber's MDA methodology for register analysis\n- **Principal Component Analysis**: Alternative dimensionality reduction approaches with visualization tools\n- **High Performance**: Built on `spaCy <https://spacy.io/models>`_ and `Polars <https://docs.pola.rs/>`_ for efficient text processing\n- **End-to-End Pipeline**: From raw text files to statistical analysis in just a few lines of code\n- **Comprehensive Visualization**: Built-in plotting functions for exploratory data analysis\n\n**Applications:**\n\n- Register and genre analysis in corpus linguistics\n- Text classification and machine learning preprocessing \n- Diachronic language change studies\n- Cross-linguistic variation research\n- Academic writing analysis and pedagogical applications\n- Stylometric analysis and authorship attribution\n\nThe package uses `spaCy <https://spacy.io/models>`_ part-of-speech tagging and dependency parsing with `Polars <https://docs.pola.rs/>`_ DataFrames for high-performance analytics.\n\n**Accuracy Note**: Feature extraction builds from probabilistic taggers, so accuracy depends on model quality. Texts with irregular spellings or non-standard punctuation may produce unreliable outputs unless taggers are specifically tuned for those domains.\n\nSee `the documentation <https://browndw.github.io/pybiber>`_ for comprehensive guides and API reference.\n\nSee `pseudobibeR <https://cran.r-project.org/web/packages/pseudobibeR/index.html>`_ for the R implementation.\n\nQuick Start\n-----------\n\n**One-line processing** from a folder of text files:\n\n.. code-block:: python\n\n import pybiber as pb\n\n # Process all .txt files in a directory\n pipeline = pb.PybiberPipeline(model=\"en_core_web_sm\")\n features = pipeline.run_from_folder(\"path/to/texts\")\n\n**Multi-Dimensional Analysis** with visualization:\n\n.. code-block:: python\n\n # Create analyzer for statistical analysis\n analyzer = pb.BiberAnalyzer(features)\n \n # Perform MDA and generate scree plot\n mda_results = analyzer.mda()\n analyzer.mdaviz_screeplot()\n \n # Plot group means by dimension\n analyzer.mdaviz_groupmeans(grouping_var=\"register\")\n\nInstallation\n------------\n\nYou can install the released version of pybiber from `PyPI <https://pypi.org/project/pybiber/>`_:\n\n.. code-block:: install-pybiber\n\n pip install pybiber\n\nInstall a `spaCY model <https://spacy.io/usage/models#download>`_:\n\n.. code-block:: install-model\n\n python -m spacy download en_core_web_sm\n\nUsage\n-----\n\n**Data Requirements**\n\nThe pybiber package works with corpora structured as DataFrames with:\n- ``doc_id`` column: Unique document identifiers \n- ``text`` column: Raw text content\n\nThis follows conventions from `readtext <https://readtext.quanteda.io/articles/readtext_vignette.html>`_ and `quanteda <https://quanteda.io/>`_.\n\n**Step-by-Step Workflow**\n\n1. **Import libraries and load spaCy model**:\n\n.. code-block:: python\n\n import spacy\n import pybiber as pb\n from pybiber.data import micusp_mini # Sample corpus\n \n nlp = spacy.load(\"en_core_web_sm\")\n\n2. **Parse corpus with spaCy**:\n\n.. code-block:: python\n\n # Parse texts to extract linguistic annotations (modern approach)\n processor = pb.CorpusProcessor()\n tokens_df = processor.process_corpus(micusp_mini, nlp)\n\n3. **Extract Biber features**:\n\n.. code-block:: python\n\n # Aggregate 67 linguistic features per document \n features_df = pb.biber(tokens_df)\n\n4. **Advanced Analysis** (optional):\n\n.. code-block:: python\n\n # Statistical analysis and visualization\n analyzer = pb.BiberAnalyzer(features_df)\n \n # Multi-Dimensional Analysis\n mda_results = analyzer.mda()\n \n # Principal Component Analysis\n pca_results = analyzer.pca()\n \n # Visualization options\n analyzer.mdaviz_screeplot() # Eigenvalue plot\n analyzer.pcaviz_contrib() # Feature contributions\n analyzer.mdaviz_groupmeans(group_var=\"genre\") # Group comparisons\n\n**Pipeline Convenience Functions**\n\nFor streamlined processing, use the high-level pipeline:\n\n.. code-block:: python\n\n from pybiber import PybiberPipeline\n \n pipeline = PybiberPipeline(model=\"en_core_web_sm\", disable_ner=True)\n \n # From folder of .txt files\n features_df = pipeline.run_from_folder(\"/path/to/texts\")\n \n # From in-memory corpus\n features_df, tokens_df = pipeline.run(corpus_df, return_tokens=True)\n \n # One-liner convenience functions\n features_df = pb.run_biber_from_folder(\"/path/to/texts\")\n features_df = pb.run_biber(corpus_df)\n\nFeature Categories\n------------------\n\nThe package extracts 67 linguistic features across 16 categories:\n\n- **Tense & Aspect**: Past tense, perfect aspect, present tense\n- **Adverbials**: Place and time adverbials \n- **Pronouns**: 1st/2nd/3rd person, demonstrative, indefinite pronouns\n- **Questions**: Direct wh-questions\n- **Nominal Forms**: Nominalizations, gerunds, nouns\n- **Passives**: Agentless and by-passives\n- **Stative Forms**: *be* as main verb, existential *there*\n- **Subordination**: 18 different clause types (that-clauses, wh-clauses, infinitives, relatives, etc.)\n- **Modification**: Prepositional phrases, attributive/predicative adjectives, adverbs\n- **Lexical Specificity**: Type-token ratio, word length\n- **Lexical Classes**: Conjuncts, hedges, amplifiers, emphatics, discourse particles\n- **Modals**: Possibility, necessity, and predictive modals\n- **Specialized Verbs**: Public, private, suasive verbs\n- **Reduced Forms**: Contractions, deletions, split constructions\n- **Coordination**: Phrasal and clausal coordination\n- **Negation**: Synthetic and analytic negation\n\nSee the `full feature list <https://browndw.github.io/pybiber/feature-categories.html>`_ for detailed descriptions.\n\nPerformance & Requirements\n--------------------------\n\n**System Requirements:**\n- Python 3.10+\n- spaCy model with POS tagging and dependency parsing (e.g., ``en_core_web_sm``)\n\n**Performance Notes:**\n- Built on Polars for fast DataFrame operations\n- Supports multiprocessing for large corpora\n- Memory-efficient processing with configurable batch sizes\n- Processing time: ~20-30 seconds for small corpora (e.g., 500 documents)\n\nLicense\n-------\n\nCode licensed under the `MIT License <https://opensource.org/license/mit/>`_.\nSee the `LICENSE <https://github.com/browndw/pybiber/blob/master/LICENSE>`_ file.\n\n.. |pypi| image:: https://badge.fury.io/py/pybiber.svg\n :target: https://badge.fury.io/py/pybiber\n :alt: PyPI Version\n\n.. |pypi_downloads| image:: https://img.shields.io/pypi/dm/pybiber\n :target: https://pypi.org/project/pybiber/\n :alt: Downloads from PyPI\n\n.. |tests| image:: https://github.com/browndw/pybiber/actions/workflows/test.yml/badge.svg\n :target: https://github.com/browndw/pybiber/actions/workflows/test.yml\n :alt: Test Status\n",
"bugtrack_url": null,
"license": "MIT License\n \n Copyright (c) 2025 David Brown\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.\n \n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n \n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n \n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n \n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n \n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n \n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n \n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n \n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. 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