PyLangAcq: Language Acquisition Research in Python

PyLangAcq is a Python library primarily for language acquisition research. An important goal is allow very flexible and powerful handling of CHILDES data in any custom programs for computational modeling.

Features

  • Comprehensive capabilities of handling CHAT transcripts as used in CHILDES
  • Intuitive data structures for flexible data access and all sorts of modeling work
  • Standard developmental measures such as TTR, MLU, and IPSyn readily available
  • More benefits from Python: fast coding, numerous libraries for computational modeling and machine learning
  • Powerful extensions for research with conversational data in general

Getting started

Download and install: Get PyLangAcq!

Tutorial: Quick examples and pointers

Library reference: Full library and code details

All pages of this library documentation can be reached via the menu on the left or the Table of contents.

How to cite

PyLangAcq is maintained by Jackson L. Lee. If you use PyLangAcq in your research, please cite the following:

Jackson L. Lee, Ross Burkholder, Gallagher B. Flinn, and Emily R. Coppess. 2016. Working with CHAT transcripts in Python. Technical report TR-2016-02, Department of Computer Science, University of Chicago. [code]

@TechReport{lee-et-al-pylangacq:2016,
  Title       = {Working with CHAT transcripts in Python},
  Author      = {Lee, Jackson L. and Burkholder, Ross and Flinn, Gallagher B. and Coppess, Emily R.},
  Institution = {Department of Computer Science, University of Chicago},
  Year        = {2016},
  Number      = {TR-2016-02},
}

Technical support, library development, etc.

If you have any questions, comments, bug reports etc, please open issues at the GitHub repository, or contact Jackson L. Lee.

Changelog on GitHub