The phrase "article classification" is correct and usable in written English.
It can be used in contexts related to categorizing or organizing articles, such as in academic writing, data analysis, or content management.
Example: "The article classification process is essential for organizing research papers in the database."
Alternatives: "document categorization" or "content classification".
(ii) The tagging process consists of two steps: article classification and tag selection.
In this article, classification made on the parametric improvement achieved by adopting CLDs.
The obtained term-weight matrix will be used for article classification to find its relevant category/domain.
To compare both unipartite and bipartite partitions with the external article classification, we will be focussed only on the articles that fall into each cluster.
The latter deviation from the article classification, which is not explained by category co-occurrence, does not exclude that similarities between these topics exist but are considered not strong enough by the authors to label the articles with both subject classes.
Results from manual article classification were used to identify the subjects of articles related to peaks in news volume.
Sufficient agreement between co-authors deemed crosschecking more than 20% of article classifications unnecessary.
For this purpose, Figure 6 summarizes the different steps used in this article for classification process.
Therefore, in this article, the classification of plant-based raw materials according to their characteristics is discussed.
"Nobody would go after this article on classification grounds unless they were pursuing a political agenda, and it is amazing to attack someone politically for writing an article in support of a policy of the president of the United States," said Matthew Bunn, a former White House official under President Clinton and now a nonproliferation expert at Harvard's Kennedy School of Government.
We tried three approaches in this article, (i) classification (to distinguish partial positives with negatives); (ii) ranking (to rate partial positive more likely than negatives); (iii) embedding (to make data clusters get similar labels).
