Exact(4)
Open image in new window Fig. 11 Keywords groups based on use of terms in Abstracts per level of income.
Open image in new window Fig. 12 Keywords groups (based on use of terms in Abstracts) per year of publication.
Twenty-one relation keywords related to the above five relation keywords groups were removed from the relation keyword dictionary as these keywords extract many false PPIs than true PPIs.
In the present improved version, we modified the PPI extraction methodology by incorporating the following three major updates: Twenty-one relation keywords related to the above five relation keywords groups were removed from the relation keyword dictionary as these keywords extract many false PPIs than true PPIs.
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Table 7 Settings of the evaluations on Weka algorithms Attribute selection No Yes Frequencies of keywords grouped by class 1A 1B Frequencies of keywords grouped with the frequencies of their variants 2A 2B Frequencies of keywords with information gain grouped by class 3A 3A.
One can then ask why certain keywords group together, and also, given that they are selected as a group, what their selective 'fitness' is relative to other groups [28].
Our search algorithm considered permutations and combinations of keywords, grouped by category.
Analysing these previous keyword groups per level of income and per year, as shown in Fig. 11, the keyword group related to ICT in government is the one which is most often mentioned in all of the types of level of income per country.
Google has a thing called a contextual targeting tool that provides you with keyword groups and the historical costs associated with advertising for those specific keywords.
First, GILs were distributed into keyword and non-keyword groups for five different keywords.
The ratio of these enriched terms between keyword and non-keyword groups can be found in Figure 5.
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