We also include an articial task constructed specically to exhibit feature
importance that varies locally at the class level, and several problems from natural
language processing (NLP) Construct is an
articially constructed 200-instance data set designed to showcase the strength
of the CDW algorithm. It consists of ten features with random values from 0 to
9, with one feature set at random to 10. The class of an instance is the number
of the feature that is set at ten. POS, Gen-Sem, Spec-Sem, and Concept are NLP data sets of unknown words and are described in detail in Cardie Brie
y, the learning task involves predicting the part of speech, general and
specic semantic class, and concept activation respectively for unknown words
drawn from the MUC business joint venture corpus In addition
to the class value, each case is described by 34 features that encode information
about the local and global context of the unknown word
We also include an arti cial task constructed speci cally to exhibit featureimportance that varies locally at the class level, and several problems from naturallanguage processing (NLP) Construct is anarti cially constructed 200-instance data set designed to showcase the strengthof the CDW algorithm. It consists of ten features with random values from 0 to9, with one feature set at random to 10. The class of an instance is the numberof the feature that is set at ten. POS, Gen-Sem, Spec-Sem, and Concept are NLP data sets of unknown words and are described in detail in Cardie Briey, the learning task involves predicting the part of speech, general andspeci c semantic class, and concept activation respectively for unknown wordsdrawn from the MUC business joint venture corpus In additionto the class value, each case is described by 34 features that encode informationabout the local and global context of the unknown word
การแปล กรุณารอสักครู่..
