TiFi: Taxonomy Induction for Fictional Domains[Extended version]
Table 6. Step 2 - Edge cleaning: Proper-name vs. concept edges.
['[BOLD] Method', '[BOLD] Universe', '[BOLD] Proper-name edges  [BOLD] Precision', '[BOLD] Proper-name edges  [BOLD] Recall', '[BOLD] Proper-name edges  [BOLD] F1-score', '[BOLD] Concept edges  [BOLD] Precision', '[BOLD] Concept edges  [BOLD] Recall', '[BOLD] Concept edges  [BOLD] F1-score']
As mentioned earlier, taxonomy induction on real-world domain can leverage a lot of semantic knowledge like WordNet synsets, while fiction frequently contains non-standard categories such as Valar and Tatyar. We further evaluate the performance of TiFi by distinguishing two types of edges: Concept edges: Both parent and child exist in WordNet. Proper-name edges: At least one of parent and child does not exist in WordNet. In The Lord of the Rings, there are 145 proper-name edges and 407 concept edges, while in Game of Thrones, there are 61 and 329 of each, respectively. As one can see, for proper-name edges, TiFi achieves a very high precision of 92%/96%, outperforms HEAD by 4%/10% and HyperVec by 14%/53% in F1-score, respectively.