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So it is a single-label dataset in our learning problem.

Since many of the compounds are experimental, have not been tested for human consumption and covers high diversity therefore, we believe it would be good choice to include this dataset in our study.

In this work, we also use a large-scale dataset, in our case data from a popular LBSN, which expresses user preferences for venues in a region, for various regions around the globe.

Column CK from Table 3 exhibits the level of agreement of each dataset in our evaluation by means of Cohen's Kappa, an extensively used metric to calculate inter-anotator agreement.

We investigate each dataset in our statistical pipeline and tweak various parameters.

Missing data or incomplete sequences did not, however, affect the inferred phylogeny because the dataset in our study provided sufficient information, consistent with previous empirical studies [22], [31], [32].

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We use Turkish and English review datasets in our experiments.

We have considered the subset of the entire datasets in our experiment.

The datasets in our case studies are fairly diverse in topicality, time span, and size, as shown in Table 1.

For both datasets in our evaluation, we estimate (MA = 10) ppm, (m_{ MA } = 200) Da, and (sigma _{text{m}}= 10) by manual inspection of the data.

We use four datasets in our experiments: the Paris users and the London users for Paris attacks, the Brussels users and the San Bernardino users.

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