| Question asked
|
# In the paper under local approach weigh …
# In the paper under local approach weights are assigned to edge types. i.e. if nodes x and y are connected by edge label l irrespective of x and y this edge has same weight for all such x and y. My question is, that by this aren't we loosing some local information? Saying two FBI agents are friends has very different significance from two terrorists being friends and much more when a terrorist and FBI agent are friends (it may not be realistic, is used to only convey the idea).
# Could loosing local information, mentioned above, be one of the reasons why local approach has not performed as well as global? For the given evaluation isn't it easy to come up with useful 'edge-label n-grams' or 'top edge-label n-grams' that could significantly boost precision after learning? i.e. Do you think that, since the relations that evaluation is looking for in the individual datasets are very few and narrow, it is easy to come up with few n-grams that can help improve the precision? grams that can help improve the precision?
|
