Multi-label Categorization of Accounts of Sexism using a Neural Framework
Table 2: Results with traditional machine learning (Label Powerset)
['Features⟶ Classifier↓', 'Word n-grams  [ITALIC] FI', 'Word n-grams  [ITALIC] Fmacro', 'Word n-grams  [ITALIC] AccI', 'Word n-grams  [ITALIC] Fmicro', 'Character n-grams  [ITALIC] FI', 'Character n-grams  [ITALIC] Fmacro', 'Character n-grams  [ITALIC] AccI', 'Character n-grams  [ITALIC] Fmicro', 'Averaged ELMo vectors  [ITALIC] FI', 'Averaged ELMo vectors  [ITALIC] Fmacro', 'Averaged ELMo vectors  [ITALIC] AccI', 'Averaged ELMo vectors  [ITALIC] Fmicro', 'Composite features  [ITALIC] FI', 'Composite features  [ITALIC] Fmacro', 'Composite features  [ITALIC] AccI', 'Composite features  [ITALIC] Fmicro']
(word n-grams, character n-grams, averaged ELMo vectors, and composite features). We use Label Powerset for these methods, since the direct (non-transformative) formulation cannot be used with them. Among these combinations, logistic regression with averaged ELMo embeddings as features performs the best.