W01-0807 |
be achieved by making use of a
|
rule generalization
|
hierarchy . Then , the statements
|
P13-2069 |
address the above issues with
|
rule generalization
|
, then consider all the permutations
|
P07-1105 |
. Similarly , the goal of the
|
rule generalization
|
step is to obtain a new target
|
D11-1020 |
categories . Figure 5 illustrates the
|
rule generalization
|
process under these restrictions
|
W97-0110 |
user 's needs . In this way ,
|
rule generalization
|
makes the customization for a
|
W97-0809 |
processing new information . This
|
rule generalization
|
process will be described in
|
A97-2004 |
articles from the domain . The
|
Rule Generalization
|
routines , with the help of WordNet
|
P11-1003 |
baseline system . 1 Introduction
|
Rule generalization
|
remains a key challenge for current
|
W97-0117 |
independence can be introduced through
|
rule generalization
|
. Furth research and evaluations
|
W97-0809 |
respectively , address training ,
|
rule generalization
|
, and the scanning of new information
|
P07-1105 |
generalized from a grammar , using the
|
rule generalization
|
step . In Figure 2 , the grammar
|
W00-1436 |
background and the procedure of
|
rule generalization
|
is described in detail in ( Wanner
|
J91-2002 |
distance of ± 5 . Example
|
rule generalization
|
session with the computer system
|
W97-0809 |
paper describes the automated
|
rule generalization
|
method and the usage of WordNet
|
P07-1105 |
grammars specialized from . The only
|
rule generalization
|
steps allowed in the grammar
|
C02-1080 |
within the entities , and performed
|
rule generalization
|
using POS ( part-of-speech )
|
W97-0110 |
op - erations : rule creation ,
|
rule generalization
|
and rule ap - plication . Rule
|
A97-2004 |
system : the Training Process ,
|
Rule Generalization
|
, and the Scanning Pro- cess
|
I05-2033 |
et al. , 2003 ) used a di erent
|
rule generalization
|
method called RGLearn . Row 4
|
W07-0731 |
Data-Oriented Parsing inspired
|
rule generalization
|
technique proposed by Chiang
|