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Exact(19)
For each relation, two types of features are extracted.
A traditional RE system searches for specific patterns for each relation.
Then, for each relation r type, using Eq. (6), it computes the belief masses combination.
To achieve this goal, we further analyzed the errors for each relation.
For each user, the algorithm starts by counting the number of occurrences for each relation or interaction pattern.
Hence, for each relation type (r in R), in addition to the influence degree (d_r), a mass function (m_r) is associated.
Similar(41)
The authors compute the value of each relation for 6 million users and compare them.
The value concept mapping function φ is implemented as σ A = c (π A (r)) for each attribute A of each relation r in Δ.
Table 1 highlights regions showing activation for each derived relation relative to Foils. Figure 3 shows that Transitive and Equivalence relations elicited bilateral activation in the anterior hippocampus, which overlapped considerably (see insert), while Symmetrical relations elicited activation in the nearby parahippocampus.
For each candidate relation, DeepDive computed a probability that the relation was a correct interaction.
According to Fader et al. [5] 'typically, IE systems learn an extractor for each target relation from labeled training examples'.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com