As a fan of dense New York Times-style crosswords, I challenged myself to create topic-specific puzzles. It turns out that generating crosswords and efficiently placing words is a non-trivial computational problem.
I started the project, "Joystick Jargon" combining traditional crossword elements with gaming-related vocabulary. Here's the technical process behind it:
1. Data Source: Used a 3.8 Million Rows Reddit dataset from Hugging Face (https://huggingface.co/datasets/webis/tldr-17).
2. Data Filtering: Narrowed down to gaming-related subreddits (r/gaming, r/dota2, r/leagueoflegends).
3. Keyword Extraction: Employed ML techniques, specifically BERT-embeddings and cosine similarity, to extract keywords from the subreddits.
4. Data Preprocessing: Cleaned up data unsuitable for crossword puzzles.
5. Grid Generation: Implemented a heuristic crossword algorithm to create grids and place words efficiently.
6. Clue Generation: Utilized a Large Language Model to generate context-aware clues for the placed words.
The resulting system creates crossword puzzles that blend traditional elements with gaming terminology, achieving about a 50-50 mix.
This project is admittedly overengineered for its purpose, but it was an interesting exploration into natural language processing, optimization algorithms, and the intersection of traditional word games with modern gaming culture.
A note on content: Since the data source is Reddit, some mature language may appear in the puzzles. Manual filtering was minimal to preserve authenticity.
You can try the puzzles here: <https://capsloq.de/crosswords/joystick-jargon>
I'm curious about the HN community's thoughts on this approach to puzzle generation? What other domains might benefit from similar computational techniques for content creation?