The world's first high school computational linguistics hackathon.

LingHacks is California's first computational linguistics-themed hackathon (24-hour coding event) series for high schoolers. Our goal is to expose students of all backgrounds to the field of natural language processing and inspire students to pursue computer science in their careers. ​LingHacks is a 24-hour invention competition where you come together with a team and build a software project that integrates computational linguistics and solves a scientific or social problem.

Computational linguistics, otherwise known as natural language processing, is the field of artificial intelligence that applies to the synthesis and analysis of language and speech. Things like machine translation technologies, voice assistants, search engines, and chatbots are all powered by computational linguistics tools. It's a fascinating synergy of scientific techniques applied to an elegant humanity that is part and parcel of our core human identities. 

 

 

View full rules

Eligibility

High school students at or over the age of 13 at any experience level are welcome! There is no linguistics or programming experience required! We have designed workshops and will have mentors onsite to help you gain skills in programming, computational linguistics, and research. 

Teams can be between 1 and 4 people, and you don't need to have a team or an idea beforehand--we'll have ideation and team-forming sessions!

Judges

Shervin Oloumi

Shervin Oloumi
Corporate Engineer, Google

Karen Kincy

Karen Kincy
Software Engineer, Google

Alicia Wong

Alicia Wong
WBB Student, University of Southern California

Mimee Xu

Mimee Xu
Machine Learning Engineer, UnifyID

Paul Cousineau

Paul Cousineau
Director - Household Organization, Alexa, Amazon

Allan Huang

Allan Huang
EECS Student, UC Berkeley

Andrew Chang

Andrew Chang
Computer System Architect, Samsung

Jacopo Daeli

Jacopo Daeli
Lead Software Engineer, GoDaddy

Catherine Yeo

Catherine Yeo
Founder, PixelHacks

Judging Criteria

  • Creativity
    Do other things like this exist? Is it a new and unexpected application of NLP?
  • Practicality
    Does it solve a real-world problem? Could it be implemented in the near future?
  • Complexity
    How difficult was it to make the project?
  • Potential
    Does it have the potential for long-term research and development?
  • Research
    Was the idea thoroughly researched? Were sources and previous research cited? Are potential pitfalls and workarounds acknowledged?
  • Understanding
    Was the project and all accompanying algorithms thoroughly explained?
  • Completeness
    Is your algorithm or model reliable? Is it accurate and precise?