| P98-1093 | to become a branch function for | episodic retrieval | . It is possible to arrange each |
| P98-1093 | are used as retrieval keys for | episodic retrieval | , and as axes for multi-dimensional |
| P98-1093 | The model has three functions : | episodic retrieval | , multi-dimensional clas - sification |
| P98-1093 | , this idea is similar to our | episodic retrieval | though the purpose and target |
| P98-1093 | Retrieval The 5W1H index can easily do | episodic retrieval | by choosing a set of related |
| P98-1093 | predicate " element tg ( produce ) are | episodic retrieval | keys . The extracted results |
| P98-1093 | platform with three functions : | episodic retrieval | , multidimensional classification |
| P98-1093 | platform with three func - tions : | episodic retrieval | , multi-dimensional classi - |
| P98-1093 | classification ( Figure 1 ) . 3.1 | Episodic Retrieval | The 5W1H index can easily do |
| P98-1093 | data and academic papers . 2 ) | Episodic retrieval | : The interface should be improved |
| P98-1093 | order to compare retrieval data . | Episodic retrieval | is based on the temporal sorting |
| P98-1093 | precision is 82.4 % . an example of | episodic retrieval | based on headline news saying |
| P98-1093 | semiconductors ) " on the headline for | episodic retrieval | . A " who " element NEC , a " |
