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For each candidate relation, we obtained judgments from five Turkers.
For each candidate relation, DeepDive computed a probability that the relation was a correct interaction.
Without distant supervision, DeepDive does not know that the candidate relation is a true interaction.
For our work, we created a random variable is_correct for each candidate relation.
In contrast, we defined a False candidate relation as a gene gene candidate relation that was either annotated in the Negatome 2.0 database (Blohm et al., 2014) or was randomly selected with no interaction evidence.
In this way, DeepDive could assign a candidate relation a probability if that relation was used for training.
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TextOntoEx analyses natural domain text to extract candidate relations and then maps them into meaning representation to facilitate constructing ontology.
In addition, Stanford's system significantly improved the information retrieval component of the system, as well as the consistency and inference procedure applied after candidate relations have been extracted.
A brief survey of the candidate-relata should thus pair them with the candidate relations that best fit each in turn (Van Gulick 2001).
A less commissive objection is that, contra Dretske, there are candidate relations besides that of representing: some wide functional relation, perhaps, or a typical-cause relation (where neither of these is itself taken to constitute representing).
It is noteworthy that different candidate relata come with different candidate relations: Whereas those who regard properties as the primary relata of reductions might coherently suggest that identity is the relevant link, those who assume that theories are the primary relata of reduction should refrain from this move: No theory reduces to itself.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com