TextXD aims to foster cross-pollination among researchers who work with natural language data, whether they identify as computer, social, data, or information scientists, including linguists. Instead of duplicating the efforts of existing groups (e.g., seminars, labs), it seeks to facilitate mutually beneficial collaborations by

  • Hosting a common calendar of existing natural language processing (NLP) groups’ activities
  • Creating a regularly scheduled "workbench" where researchers can receive open consultation on their technical questions
  • Seeding a stack exchange dedicated to NLP questions and answers
  • Hosting semesterly symposia to surface common NLP problems and new opportunities for cross-disciplinary collaboration
  • Matching NLP students to research opportunities twhere they can practice their new skills
  • And more

BIDS Affiliates


Chris Kennedy


Maryam Vareth

Radiology and Biomedical Imaging, UCSF

Heather Haveman

Sociology; Haas School of Business

Jonathan Dugan

Chief Research Officer