21https://fasttext.cc/

16

withoutwith0.00.20.40.60.81.0F1-score0.740.810.760.830.750.830.750.81CoNLLeswithoutwithF1-score0.690.820.720.80.740.780.750.76CoNLLnlwithoutwith0.00.20.40.60.81.0F1-score0.270.470.230.470.230.460.240.46WNUT2017withoutwithF1-score0.830.850.80.840.810.830.820.83Archeologywithoutwith0.00.20.40.60.81.0F1-score0.780.810.670.830.710.810.780.8AnEMwithoutwithF1-score0.820.860.820.840.830.850.830.85OntoNotesMultiHashEmbed1051CoNLL esCoNLL nlWNUT2017ArcheologyAnEMOntoNotesDataset0.00.20.40.60.81.0F1-score0.810.80.470.850.810.860.80.80.470.840.810.85spaCy(large)fastTextalmost no difference in performance. These results indicate that the vectors distributed with spaCy
provide similar performance to fastText vectors.22

B TABLES WITH COMPLETE RESULTS

MultiHashEmbed

Precision
0.83±0.01
0.83±0.00
0.53±0.02
0.83±0.01
0.66±0.01
0.84±0.02

Recall
0.82±0.01
0.82±0.01
0.25±0.04
0.82±0.01
0.57±0.02
0.83±0.03
MULTI HASH EMBEDDINGS IN SPACY