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(graehl, comments on code)
passive chart: completion of actual translation rules (X or S NT in Hiero), have
rule features. Hyperedge inserted with copy of rule feature vector
(non-sparse). Inefficient; should be postponed on intermediate parses with
global pruning; just keep pointer to rules and models must provide an interface
to build a (sparse) feat. vector on demand later for the stuff we keep.
multithreading: none. list of hyperarcs for refinement would need to be
segregated into subforest blocks and have own output lists for later merging.
e.g. bottom up count number of tail-reachable nodes under each hypernode, then
assign to workers.
ngram caching: trie, no locks, for example. for threading, LRU hashing w/ locks per bucket is probably better, or per-thread caches. probably cache is reset per sentence?
randlm worth using? guess not.
actually get all 0-state models in 1st pass parse and prune passive edges per span.
allocate cube pruning budget per prev pass
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