Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All
TABLE II: Average coherence denseness calculated for each dataset. Only documents whose number of mentions greater than 3 are considered. The results are reported with three relatedness measures: Wikipedia Link-based Measure (WLM), Normalized Jaccard Similarity (NJS) and Entity Embedding Similarity (EES).
['[BOLD] Dataset', '| [ITALIC] D|', '[ITALIC] Coh_ [ITALIC] deg [BOLD]  (theoretical) Forest', '[ITALIC] Coh_ [ITALIC] deg [BOLD]  (theoretical) Tree', '[ITALIC] Coh_ [ITALIC] deg [BOLD]  (theoretical) Dense', '[ITALIC] Coh_ [ITALIC] deg [BOLD]  (calculated) WLM', '[ITALIC] Coh_ [ITALIC] deg [BOLD]  (calculated) NJS', '[ITALIC] Coh_ [ITALIC] deg [BOLD]  (calculated) EES']
We consider only the documents having at least 4 mentions because the ones with 3 or fewer mentions will lead to a fixed denseness score by the calculation described above. It is also worth mentioning that for short text datasets like KORE50 or Micro14, the edge filtering is more likely to prune the entity graph into a tree-like form or forest-like form, leading to the bias in the denseness score. The same result is observed in all settings of relatedness measures (WLM, NJS, and EES). Especially, in long text datasets like MSNBC and AQUAINT, each mentioned entity is highly related to only 3-5 other entities (by the NJS measure) although the number of entities in each document in the two datasets is more than 13 (on average). The result reveals that not all entities mentioned in a document are densely related to each other; therefore, considering all the pairwise connections is not necessary for collective entity disambiguation.