D08-1093 </title> Sujith Ravi Kevin Abstract Statistical parsers have become increasingly accurate
C02-1126 treebanks Abstract Many recent statistical parsers rely on a preprocessing step
D09-1088 Sima&#8217;an Remko Scha Abstract Applying statistical parsers developed for English to languages
C02-1132 word clusters are used in the statistical parsers of Charniak ( 1997 ) and Magerman
D10-1066 hypotheses . All of the commonly used statistical parsers use context-free dynamic programming
C00-1060 speed of our parser . Existing statistical parsers are quite etficient compared
C04-1021 fifteen years or so . However , statistical parsers require training on a massive
D10-1066 Johnson , 1991 ) In this era of statistical parsers it is useful to think in terms
D10-1069 for as long as there have been statistical parsers , but typically does not work
C04-1180 performance . 3 The Parser A number of statistical parsers have recently been developed
C04-1021 leverage continuing improvements to statistical parsers . Sublanguages The early work
C02-1075 other state-of-the-art PCFG-based statistical parsers , since different training and
D08-1022 To make things worse , modern statistical parsers are often trained on domains
C04-1021 first to provide evidence that statistical parsers can support NLIs such as PRECISE
C02-1126 Many of the recent , successful statistical parsers have made use of lexical information
C04-1180 robustness recently achieved by statistical parsers ( e.g. Collins ( 1999 ) , Charniak
D09-1080 were manually parsed . In many statistical parsers , new structures are generated
C02-1126 with heuristics , those used in statistical parsers tend not to be data-sensitive
C04-1021 We discuss the issues in using statistical parsers to build database-independent
C00-1051 ahnost no work that evaluates statistical parsers according to their coverage-accuracy
hide detail