XREF: Entity Linking for Chinese News Comments with Supplementary Article Reference
Table 7: Results on plural pronominal mentions for Ent domain. K indicates the number of entities in the gold-standard. Significant better results than all comparisons are marked with \ast (p<0.0001, approximation randomization test).
['[EMPTY]', '[BOLD] Acc@K', '[BOLD] MRR', '[BOLD] NDCG']
Results on Plural Pronominal Mentions. Though rarely studied in prior work ji2016overview, it is common to observe pronominal mentions linked to multiple entities in social media. We assume the true number of entities is given as K, which varies among samples; top K candidates output by each model are compared against the gold-standards. In addition to accuracy@K and MRR , Normalized Discounted Cumulative Gain (NDCG) jarvelin2002cumulated that considers multiple target predictions is reported. As can be seen, XREF with article entity attention significantly outperforms other comparisons. This is likely because plural nominal often refers to the entities in the article, suggesting the effectiveness or article attentions in these samples. Further experiments show that the full model with additional comment attention and features actually yields marginally lower scores.