Beats CKS03 on mnist. shrink and forget oldest svs.

(***) Zettlemoyer and Collins 2005: map sentences to typed lambda expressions: which rivers run through states that border the state with the capital austin (lambda $0:e (and:<t*,t> (river:<r,t> $0) (exists:<<e,t>,t> (lambda $1:e (and:<t*,t> (state:<s,t> $1) (next_to:<lo,<lo,t>> $1 (the:<<e,t>,e> (lambda $2:e (and:<t*,t> (state:<s,t> $2) (capital2:<s,<c,t>> $2 austin_tx:c))))) (loc:<lo,<lo,t>> $0 $1)))))) In addition to the training set of pairs like above they have two additional sources of information given by hand: a. GENLEX rules: to generate candidates for the lexicon, they produce a cross product of (all substrings of the sentence) x (all categories that can be produced from the lambda expression). The second part is done using 10 heuristic rules that convert parts of the lambda expression to possible categories e.g. N:(lambda (x) (major x)). b. Initial lexicon: this includes two types of manually added entries: domain specific, database derived entities such as (Utah => NP:utah) and domain independent entries such as “what” => (S/(S\NP))/N:(lambda f (lambda g (lamba x (and (f x) (g x)))). Both these look like cheating. How sensitive are the results to these manual inputs? What kinds of words are actually learnt at the end? Is it possible to learn entries like “what” at all? Why do we need syntactic categories, can’t we just have lambda expressions? Should look at how future work handled these. Geo880 p=.9625 r=.7929 f=.8695 (TM01 p=.8992 r=.7940) Jobs640 p=.9736 r=.7929 f=.8740 (TM01 p=.9325 r=.7984)

forget the sv that has min impact on test set?

Too much story, too little math.

This paper shows how the experimental study of artificial reduced language systems can shed light on evolutionary questions, when placed alongside evidence from other simpler language systems such as early child language.

This is a test note.