Yes, I will be working on these items.I will have to do > http://www.apache.org/licenses/cla-corporate.txt (if you work on the > code during your day job) with zvents.com
Boris > Date: Mon, 15 Aug 2011 21:02:07 +0200 > From: [email protected] > To: [email protected] > Subject: Re: to map trees to logical forms > > Hello, > > thanks for that. > > Should we proceed with the contribution? > > The next steps are roughly as follows: > - Create a jira issue for the contribution, and attach the source code to it > - Do a vote to accept it on the dev list > - Do IP clearance > > IP clearance will most likely include signing these papers: > http://www.apache.org/licenses/software-grant.txt > http://www.apache.org/licenses/icla.txt > http://www.apache.org/licenses/cla-corporate.txt (if you work on the > code during your day job) > > After we are through these steps we can import the code into our > subversion repository. > > Jörn > > On 8/15/11 8:39 PM, Boris Galitsky wrote: > > 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 > >
