Text Categorization with Class-Based and Corpus-Based Keyword Selection

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Text Categorization as a Graph Classification Problem (2015, methodology): “In some sense, it is similar to what Özgür et al. (2005) proposed with corpus-based and class-based keyword selection for text classiﬁcation except that we use here document-based keyword selection following the approach from Rousseau and Vazirgiannis (2015).”
SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (2019, methodology): “In order to provide a measure that is independent on the class size, the submissions will be ranked by macro-averaged F1-score, computed as described in (Özgür et al., 2005).”
Findings of the 2013 Workshop on Statistical Machine Translation (2013, methodology): “…(macro averaging) or by micro averaging in which case precision and recall are first computed by accumulating the relevant values for all classes (Özgür et al., 2005), e.g. precision = tpK + tpC (tpK + fpK) + (tpC + fpC) The latter gives equal weight to each example and is therefore…”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “To address this issue, we report the Macro-Averaged-F-Measure that is the mean of F-measures calculated for each of the two classes (Gunduz et al., 2017; ¨Ozg¨ur et al., 2005).”