Hi Jason and Jörn I will briefly comment on how our approach is different from the authors below:http://www.cs.utexas.edu/~ai-lab/downloadPublication.php?filename=http://www.cs.utexas.edu/users/ml/papers/kim.coling10.pdf&citation=In+%3Ci%3EProceedings+of+the+23rd+International+Conference+on+Computational+Linguistics+%28COLING+2010%29%3C%2Fi%3E%2C+543--551%2C+Beijing%2C+China%2C+August+2010.Sure, having something that maps trees to logical forms would be useful.
Boris, I would recommend you look at papers in Ray Mooney's group on semantic parsing: http://www.cs.utexas.edu/~ml/publications/area/77/learning_for_semantic_parsing > "The authors align naturallanguage sentences to their correct meaning > representations given the ambiguous supervision provided by a grounded language acquisition scenario".This approach takes a vertical domain, applies statistical learning and learns to find a better meaning representation, taking into account, in particular, parsing information. Mooney's et al approach cant directly map a syntactic tree structure into a logic form 'structure', at least it does not intend to do so. If a vertical domain changes, one have to re-train. It is adequate for a robocap competition but not really for an industrial app in a horizontal domain, in my opinion. What we are describing/proposing does not go as high semantically as Mooney et al, but it is domain - independent and is directly (in a structured, not statistical) way linked to syntactic parse tree, so a user does not have to worry about re-training. After training, if we have a fixed set of meaning (meaning representations in Mooneys' terms), his system would give a higher accuracy than ours, but his settings are not really plausible for industrial cases like search relevance and text relevance in a broader domain. What we observed is that overlap of syntactic tree, properly transformed, is usually good enough to accept/reject relevance >In particular, Ruifang Ge (who is now at Facebook) did phrase structure to >logical form learning: http://www.cs.utexas.edu/~ai-lab/pub-view.php?PubID=126959 I definitely enjoyed reading the phd thesis, nice survey part! Earlier work of Mooney at al used Inductive Logic Programming to learn commonalities between syntactic structure. Our approach kind of takes it to extreme: syntactic parse trees are considered a special case of logic formulas and Inductive Logic Programming 's anti-unification is defined DIRECTLY on syntactic parse trees.I am more skeptical about universality of 'semantic grammar' unless we focus on a given text classification domain. So my understanding is lets not go too far up in semantic representation unless the classification domain is fixed, there is no such thing as most accurate semantic representation for everything (unless we are in a so restricted domain as specific database querying). So I can see "Meaning Representation Language Grammar" as a different component of openNLP, but it is hard for me to see how a search engineer (not a linguist) can just plug it in and leverage it in an industrial application. RegardsBoris
