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 lled in. In our system, \expert modules" specialize in solving speci c types of clues, drawing on ideas from information retrieval, database search, and machine learning. Each expert module generates a (possibly empty) candidate list for each clue, and the lists are merged together and placed into the grid by a centralized solver. We used a probabilistic representation throughout the system as a common interchange language 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 puzzles 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 15 puzzle, making Proverb a better-than-average cruciverbalist (crossword solver).