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An academic issues on "How to estimate the entity type distributions with relation class is not known" #9
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In your example, we don't know this instance contains the relation "per:country_of_birth" in advance. Here we just initialize the virtual type words according to the pre-defined relation classes rather than estimate the prediction with the prior distributions. So there is no “the chicken or the egg?” problem here. You can refer to the "issue 遇到问题求助 #1" for specific examples of the prior distributions over the candidate set. |
Thanks for your reply ! |
The calculation is according to pre-defined categories, for example, there are only two categories: "per:birth_of_place", and "per:birth_of_data". Thus, for [sub], p("person"): 1; for [obj],p("place"): 0.5, p("data"): 0.5. You can read code for a deeper understanding. |
Thanks for your reply! |
the representation of virtual type words are statistics initialized at the start, and what the model learns during training is the latent virtual type. |
It means that the representation of virtual type words in each instance would be the same at the start, right? |
Yes. |
Thanks for your patience ! |
According to your paper: you estimate the prior distributions over the candidate set C_sub and C_obj of potential entity types, according to a certain relation class, where the prior distributions are estimated by frequency statistics. But, how do you estimate the prior distributions with an unknown relation in the instance , just like “the chicken or the egg?”.
For example, the relation “per:country_of_birth” indicates the subject entity belongs to “person” and the object entity belongs to “country”. The prior distributions for C_sub can be counted as {"person":1} , but we should know this instance contains the relation "per:country_of_birth" in advance, then we can estimate the prior distributions of the candidate set.
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