P94-1045 recently augmented the parser with a statistical disambiguation module . We use a framework similar
D10-1088 considered to be the best by the Alpino statistical disambiguation model is preserved . Then , the
P09-1069 introduced by a single word , but statistical disambiguation ( cf. Sec . 6 ) uses surrounding
P05-1011 development and the evaluation of statistical disambiguation models for wide-coverage HPSG
C04-1204 and was not used for training a statistical disambiguation model . This is very different
P07-2048 the source language side , and statistical disambiguation on the target language side .
P07-2048 Transfer 5 . Root Word Transfer 6 . Statistical Disambiguation and Rescoring ( SLM ) 7 . Sentence
C00-1059 compound nouns . We have developed a statistical disambiguation method , the detail and evaluation
P06-2006 a graphstructured stack . The statistical disambiguation phase is trained on Susanne treebank
J08-1003 hand-crafted LFG for English and a statistical disambiguation component to choose the most
P90-1031 sections discussing the LOB corpus , statistical disambiguation , the parser , and our results
P09-1069 composition rules . Lastly , the statistical disambiguation model scores each possible MR
A00-2041 score assigned by the parser 's statistical disambiguation procedure described in ( Rose
D13-1033 a morphological lexicon with a statistical disambiguation model ( Hakkani-T ¨ ur et
A00-2041 analysis according to LCFLEx 's statistical disambiguation scores is included in the set
P00-1002 English and Japanese grammar , a statistical disambiguation module for the Japanese parser
P96-1046 TRAINS corpora . Unlike previous statistical disambiguation systems , this technique thus
C02-1075 about 5.8 words on average . Our statistical disambiguation method was tested on an exact
P08-1057 as a lexical resource for the statistical disambiguation of parse trees . <title> Randomized
J14-1009 and as feature designer for the statistical disambiguation model . The sketched scenarios
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