TiFi: Taxonomy Induction for Fictional Domains[Extended version]
Table 3. Step 1 - Cross-domain category cleaning.
['[BOLD] Train', '[BOLD] Test', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score']
A characteristic of fiction is variety. As our approach requires labeled training data, a question is to which extent labeled data from one domain can be used for cleaning categories of another domain. We thus next evaluate the performance when applying models trained on LoTR on the other 5 universes, and the model trained on GoT on LoTR. As one can see, F1-scores drop by only 9%/2% compared with same-domain training, and the F1-score is above 70% even for quite different domains.