  Crossword puzzles are popular word games that require not only a large
vocabulary, but also a broad knowledge of topics. Answering each clue is a
natural language task on its own as many clues contain nuances, puns, or
counter-intuitive word definitions. Additionally, it can be extremely difficult
to ascertain definitive answers without the constraints of the crossword grid
itself. This task is challenging for both humans and computers. We describe
here a new crossword solving system, Cruciform. We employ a group of natural
language components, each of which returns a list of candidate words with
scores when given a clue. These lists are used in conjunction with the fill
intersections in the puzzle grid to formulate a constraint satisfaction
problem, in a manner similar to the one used in the Dr. Fill system. We
describe the results of several of our experiments with the system.
