PROVERB: The Probabilistic Cruciverbalist

We attacked the problem of solving crossword puzzles by computer: given a set of clues and a crossword grid, try to maximize the number of words correctly filled in. In our system, "expert modules" special- ize in solving specific types of clues, drawing on ideas from information retrieval, database search, and ma- chine learning. Each expert module generates a (pos- sibly empty) candidate list for each clue, and the lists are merged together and placed into the grid by a cen- tralized solver. We used a probabilistic representation throughout the system as a common interchange lan- guage between subsystems and to drive the search for an optimal solution. PROVERB, the complete system, averages 95.3% words correct and 98.1% letters correct in under 15 minutes per puzzle on a sample of 370 puz- zles taken from the New York Times and several other puzzle sources. This corresponds to missing roughly 3 words or 4 letters on a daily 15 x 15 puzzle, making PROVERB a better-than-average cruciverbalist (cross- word solver),