A00-1014 experiments to evaluate MIMIC 's automatic adaptation capabilities . We compared MIMIC
J15-1005 discriminative learning algorithms for the automatic adaptation of annotation guidelines . To
P99-1002 problems , it is essential to develop automatic adaptation techniques . Extraction and normalization
A00-2027 find the mixed initiative and automatic adaptation aspects of its dialogue strategies
A00-2027 MIMIC 's mixed initiative and automatic adaptation capabilities . Our results show
A00-1014 system and show that MIMIC 's automatic adaptation capabilities resulted in better
J92-3009 Similarly , it is not clear that automatic adaptation of the interface necessarily
A00-1014 spoken dialogue system to allow for automatic adaptation of dialogue strategies . Fi -
N09-2038 We wish to further investigate automatic adaptation based on implicit confidence
J15-1005 successively enhanced models for automatic adaptation of annotations . After the description
A00-2027 MIMIC 's mixed initiative and automatic adaptation aspects resulted in better performance
A00-2027 both the mixed initiative and automatic adaptation aspects of the system led to
W05-1601 to NLG systems , and of making automatic adaptation to new domains from raw corpora
J15-1005 discussions and comments . <title> Automatic Adaptation of Annotations Chinese Academy
A00-2027 MIMIC 's mixed initiative and automatic adaptation features lead to more efficient
A00-2027 evaluating the mixed initiative and automatic adaptation aspects of MIMIC and analyzed
D13-1062 Jiang et al. , 2009 ) proposed an automatic adaptation method of heterogeneous annotation
W04-2809 running and , ul - timately , enable automatic adaptation to domain - shifts Figure 1 shows
A00-2027 evaluate the mixed initiative and automatic adaptation aspects of the system , and analyzed
P09-1059 insightful comments . <title> Automatic Adaptation of Annotation Standards : Chinese
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