Your English writing platform
Discover LudwigExact(7)
The participants were asked to add a relation word chosen from a drop down menu.
We first find a relation word "binding" and follow the prep_of dependency to retrieve the first protein P0.
Note that we also handle negation by checking if a negation (neg) dependency exists on either the relation word or the subject.
First, as in the example, "no interaction of P0 with P1 was found," the negation (neg) dependency must be checked on the relation word ("interaction").
In the example, P0 is the subject of "interacts," a relation word, and P1 is the prepositional modifier of "interacts," and hence P0 - P1 matches the rule.
Spelling mistakes were corrected, and features where the wrong relation word had been selected (e.g., "does in the sea") were also corrected when the intention was clear ("is in the sea").
Similar(53)
This list then underwent various stages of processing, all of which related to the features provided by the participants and left the relation words untouched.
The feature set finally selected through this process includes 27 relation words.
However, we soon observed that some important but low-frequency relation words were not captured.
The relation words were is, has, does, made of, and "…" (participants were instructed to use "…" when they wished to use any other relation).
As for the relation words, we compiled 67 keywords that clearly describe various types of interactions between a pair of proteins.
Write better and faster with AI suggestions while staying true to your unique style.
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