@inproceedings{Mueller2022,
  author    = {Mark-Christoph M{\"u}ller},
  title     = {pyMMAX2: Deep access to MMAX2 projects from Python},
  series = {Proceedings of the 14th Linguistic Annotation Workshop. December 12, 2020, Barcelona, Spain (Online)},
  editor    = {Stefanie Dipper and Amir Zeldes},
  publisher = {Association for Computational Linguistics},
  address   = {Stroudsburg, Pennsylvania},
  isbn      = {978-1-952148-33-0},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-110848},
  pages     = {167 -- 173},
  year      = {2022},
  abstract  = {pyMMAX2 is an API for processing MMAX2 stand-off annotation data in Python. It provides a lightweight basis for the development of code which opens up the Java- and XML-based ecosystem of MMAX2 for more recent, Python-based NLP and data science methods. While pyMMAX2 is pure Python, and most functionality is implemented from scratch, the API re-uses the complex implementation of the essential business logic for MMAX2 annotation schemes by interfacing with the original MMAX2 Java libraries. pyMMAX2 is available for download at http://github.com/nlpAThits/pyMMAX2.},
  language  = {en}
}