C04-1097 models differ from the previous statistical approaches in the range of input features
C02-2019 dictionary nor a training corpus . Two statistical approaches have been applied to this problem
C92-2070 theoretical framework . Other statistical approaches have required special corpora
C02-1078 ap - proaches : rule-based and statistical approaches . In rule-based approaches (
A94-1012 an integration of symbolic and statistical approaches . In order to acquire domain
A94-1012 characterized by a mixture of symbolic and statistical approaches to grammatical knowledge ac -
A00-1034 tagger by combining symbolic and statistical approaches . MaxEnt has been demonstrated
A97-1013 strengths of both linguistic and statistical approaches to NLP can be combined in a single
C02-1086 CLIR fall into two categories : statistical approaches and translation approaches .
C00-2128 vio - lations . Whether or not statistical approaches can recognize metonymy as well
C90-3038 recognition system . Traditional statistical approaches require considerable training
C00-2131 -LSB- 10 , 11 , 14 \ -RSB- , and statistical approaches . For instance , example-based
A94-1012 <title> Combination of Symbolic and Statistical Approaches for Grammatical Knowledge Acquisition
C00-1060 dependency analysis and works on statistical approaches with gramlnars . Next , we introduce
C92-1055 the language model , traditional statistical approaches , which resolve ambiguities by
C04-1141 extraction and mining using only statistical approaches ( Church and Hanks , 1990 ; Ikehara
A97-1046 supplemented by phrases obtained with statistical approaches , such as frequency counting
C92-2065 As the resources required for statistical approaches to natural language continue
C92-1055 the language model , traditional statistical approaches , which resolve ambiguities by
C90-3031 Brown et al. 1988 which use purely statistical approaches to infer translations from a
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