Unlike the related paper I chose to read , the focus paper actually had a method for evaluating the quality of their semantic results -- which is a good thing. It also seems that they have restricted their domain to a fairly small logical space, geo data, as opposed to general interest WSJ articles as in . Despite the narrower domain, they still allow for ambiguity in the parse to be solved with a probabilistic model, whereas the earlier work seems to be rule-based with a fixed set of rule annotations for each category in the CCG. On the focus paper, the results seem promising for this data set (Geo), they are comparable to the previous state of the art. Unfortunately, the authors don't give much insight on the failure modes for this data set and how they might be improved. It seems one issue is due to previously unseen words/usage causing the recall number to be low. Another issue that might be interesting to explore is how allowing new ways of splitting the logical expressions improves performance.
 Bos, J., Clark, S., Steedman, M., Curran, J. R., & Hockenmaier, J. (2004). Wide-coverage semantic representations from a CCG parser. In Proceedings of the International Conference on Computational Linguistics