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Encoding unranked trees to binary trees, henceforth called binarization, is an important method to deal with unranked trees. For each of three binarizations we show that weighted (ranked) tree au-tomata together with the binarization are equivalent to weighted unranked tree au-tomata; even in the probabilistic case. This allows to easily adapt training… (More)
We present an approach to obtain language models from a tree corpus using proba-bilistic regular tree grammars (prtg). Starting with a prtg only generating trees from the corpus, the prtg is generalized step by step by merging nonterminals. We focus on bottom-up deterministic prtg to simplify the calculations.
We prove that the class of linear context-free tree languages is not closed under inverse linear tree homomorphisms. The proof is by contradiction: we encode Dyck words into a context-free tree language and prove that its preimage under a certain linear tree homomorphism cannot be generated by any context-free tree grammar. A positive result can still be… (More)