Review of "PROVERB: The Probabilistic Cruciverbalist - G. A. Keim et. al." Harsha V. Madhyastha This paper presents a system called PROVERB which has been developed to solve crossword puzzles. The basic idea behind the system is to adopt a two-tier approach. First, the crossword puzzle is solved using a wide variety of expert modules which specialize in a range of domains such as information retrieval, probabilistic reasoning, etc. Each expert module associates a probability with every answer it proposes. These probabilistic list of answers for each clue from every expert module are then merged by another module to produce the final solution. The objective employed in merging these lists is to maximum the number of clues correctly solved - the reasoning behind this being that this is the scoring criterion in human crossword tournaments. As this problem had never been addressed before this work, the biggest contribution of this paper is that it shows that a system can be built to solve crossword. Moreover, in spite of this being the first attempt at building a crossword solver, the system obtains extremely high accuracy. This paper clearly provides motivation for other researchers to further explore this problem. Given the design choice made for the system by the authors, the other major strength of the paper is the wide range of expert modules it explores. 30 different expert modules were built which not only used several data sources but also employed varying techniques such as latent semantic indexing and n-grams. However, though the paper describes so many varied expert modules, it does just that. It only briefly touches upon how each expert module works and there is hardly any analysis comparing their relative performance. Finally, the reader is left clueless which expert module works well for what types of clues and why. An aspect of the system that I fear about is its over-dependence on the crossword database. In fact the difference in accuracy shown by the all-or-nothing analysis clearly highlights this over-dependence. Though the authors defend this approach by the fact that most human crossword solvers also become experts based on experience, this limits the system's applicability across crosswords from different sources. Hence, I believe one of the important questions to be addressed is whether a system to solve crosswords can be built without being so overly dependent on access to similar historical crosswords. Do there exist some rules that can be learnt that are valid across different crossword sources? Another interesting research question to be addressed is whether a system can be built to solve British-style cryptic crosswords, instead of the highly simplistic style of crosswords addressed in this paper. The reason I find this interesting is that unlike in the easy crosswords accounted for by PROVERB, the clues in cryptic crosswords have more information as each clue usually has two different ways of getting at the answer. So, in fact, cryptic crosswords might be easier to tame using machine power!