Exact(5)
Label consistency is based upon the hypothesis that if the same PHI word or phrase appears multiple times within a document, it likely has the same label.
We will assume that T and T' each has n labelled leaves numbered 1,..., n such that the leaf numbered x in T has the same label as the leaf numbered x in T'.
This labeling defines a leaf-mapping G, S : Le(G →Le(S) that maps a leaf node g∈Le(G) to that unique leaf node s∈Le(S) which has the same label as g.
This labeling defines a leaf-mapping L T, S : L e (T ) → L e (S ) that maps a leaf node t ∈ L e (T ) to the unique leaf node s ∈ L e (S ), which has the same label as t.
At this point, SuperFine creates a new set of source trees, by modifying each of the input source trees so that each contains at most d leaves, as follows: If x is an internal node in a source tree that is adjacent to two leaves, each of which has the same label l, then we remove its neighboring leaves and relabel x by l.
Similar(55)
Our distance measure generalizes the Robinson-Foulds (RF) distance measure to multi-labeled trees (mul-trees) or trees where multiple leaves can have the same label.
Therefore, they also have the same label (color).
Here we have an unfortunate coincidence of having the same label for two very different modes.
PathJoin assumes that no two siblings in data trees have the same label.
Nodes having the same label are returned as a detected community.
Thus, we have to rearrange these labels such that the clusters with similar centers have the same label.
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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