Second-Order Word Embeddingsfrom Nearest Neighbor Topological Features
Table 3: Results on CoNLL-03 NER task by named entity type. n is the number of entities of each type in the gold dataset. Highest performance in each metric on each entity type is marked in bold.
['Setting', 'Loc ( [ITALIC] n=1668) Pr', 'Loc ( [ITALIC] n=1668) Rec', 'Loc ( [ITALIC] n=1668) F1', 'Misc ( [ITALIC] n=702) Pr', 'Misc ( [ITALIC] n=702) Rec', 'Misc ( [ITALIC] n=702) F1', 'Org ( [ITALIC] n=1661) Pr', 'Org ( [ITALIC] n=1661) Rec', 'Org ( [ITALIC] n=1661) F1', 'Per ( [ITALIC] n=1617) Pr', 'Per ( [ITALIC] n=1617) Rec', 'Per ( [ITALIC] n=1617) F1']
Most strikingly, nearest neighbor information is critical for recognizing MISC entities, leading to 8.4% absolute increase in precision when using multi-sample second order embeddings alone, and similar increases with concatenation. Recall on MISC falls slightly when incorporating second-order information, though the noisy k=25 graph does increase recall by 0.2% absolute. The converse pattern emerges for PER entities, where precision falls by over a point when including any second-order information, but recall increases slightly with single-sample nearest neighbor information. ORG and LOC are less clear, with small variations in precision and recall.