| Age | Commit message (Collapse) | Author |
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search. This is already used by the exhaustive generator. The time to generate 10000 abstract trees with ParseEng went down from 4.43 sec to 0.29 sec.
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exception if the grammar is missing
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one for the output trees. This means that the memory for parsing can be released as soon as the needed abstract trees are retrieved, while the trees themselves are retained in the separate output pool
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partial trees
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using readline with word completion
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per sentence
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The API computes PARSEVAL and Exact Match for a given tree. As a side effect the abstract trees in Python are now compared for equality by value and not by reference
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sentences
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properly. It should be fixed but for now I just disabled the optimization
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in the C runtime
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much memory and even makes it impossible to load the Finnish and the German parsing grammars.
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defined
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flag beam_size in the top-level concrete module
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returns the name of the concrete syntax
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declarations for generic programming from data.c are removed as well
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use the generic programming API
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and adds another implementation which builds on the existing API for lexers in the C runtime. Now it is possible to write incremental Lexers in Python
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Is allows to define a tokenizer in python (or use an existing one, from nltk for instance.)
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decideable for propositional logic. dependent types and high-order types are not supported yet. The generation is still in decreasing probability order
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