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Then it builds a word co-occurrence graph on the document set and gets the ranking score of each word by PageRank.
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Some office products allow pasting the graph into the document, double-clicking on it, and setting the "transparent color": clicking on the gray background of a Berkeley Madonna graph will make it transparent.
Extracting Summary Sentences Based on the Document Semantic Graph.
begin{aligned} p v_{i}) = frac{{mathrm {tf}}(v_{i})}{sum nolimits _{i = 1}^{N} {mathrm {tf}}(v_{i})},quad i = 1, ldots, N. end{aligned} (4 Since the expanded nodes do not have matching keyterms in the document, their jump probabilities are set to be 0. We perform PPR on the document keyterm graph.
Based on domain ontology, the semantics of a document are represented by a graph (called Document Semantic Network) and, then, proposed relation-based weighting schemes are used to evaluate the graph to calculate the document relevance score.
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My toddler made a bar graph on the topic.
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Then we combine all the keyterm graphs together and obtain the keyterm graph for the input document, which is the so-called document keyterm graph.
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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