P94-1033 count . The precision rate of compound extraction will then be greatly improved
P94-1033 Cluster Classification Model for Compound Extraction The first step to extract compounds
C00-2116 about 90 % accuracy of Thai open compound extraction . However , the algorithm emphasizes
P94-1033 therefore used as a feature for compound extraction . Using both the mutual information
W00-1219 efficient and robust for Chinese compounds extraction . And , mutual information mainly
P94-1033 two supplementary features for compound extraction . Parts of Speech Part of speech
P94-1033 the words as a third feature for compound extraction . An automatic compound retrieval
P94-1033 of speech as the features for compound extraction . The problem is modeled as a
C96-2208 for data preparation and open compound extraction . The cornpetitive selection
C96-1006 . ( 1 ) Refinement of nominal compound extraction procedure : The simplified procedure
W00-1219 strict . This is very helpful for compound extraction . 3.2 Compounds Extraction from
P94-1033 both the recall and precision for compound extraction is improved . The simulation
D11-1060 Bootstrapping has been applied to noun compound extraction as well . For example , Kim and
W11-3508 1994 ) applied decision tree for compound extraction , but their supervised learning
W00-1219 two simple systems for Chinese compound extraction -- CXtract . CXtract uses predominantly
C96-1006 extraction ; refining the nominal compound extraction procedure will considerably improve
W00-1219 statistical approach to Chinese compounds extraction from very large corpora using
W00-1219 47.43 % . In this method , the compound extraction problem is formulated as classification
W00-1219 they use the method for English compounds extraction while we extract Chinese compounds
W00-1219 corpus are of interest to us in compound extraction . We use a criterion , called
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