In contrast to the models used in the paper, we manually sentence-segmented and normalize hyphenations and
trained a NER model using the German Europeana BERT model.
Additionally, we perform experiments with different context sizes. This approach is described in
more detail in
this paper
.
Results
The results with different context sizes can be seen in the following table:
Model
Run 1
Run 2
Run 3
Run 4
Run 5
Avg.
German Europeana BERT
(81.45) / 76.92
(
81.53
) / 77.03
(80.49) / 77.83
(80.88) / 77.19
(81.39) / 77.00
(81.15 ± 0.45) / 77.19 ± 0.34
German Europeana BERT (16)
(
82.56
) / 77.38
(81.19) / 77.76
(80.99) / 76.34
(81.27) / 77.70
(81.28) / 77.22
(81.46 ± 0.63) / 77.28 ± 0.57
German Europeana BERT (32)
(
82.04
) / 78.50
(81.14) / 76.56
(81.81) / 78.28
(81.50) / 76.90
(81.64) / 77.94
(81.63 ± 0.34) / 77.64 ± 0.86
German Europeana BERT (64)
(81.21) / 78.39
(81.27) / 75.98
(
81.88
) / 78.40
(81.66) / 77.35
(81.29) / 76.70
(81.46 ± 0.29) / 77.36 ± 1.06
German Europeana BERT (80)
(82.13) / 77.77
(81.31) / 76.81
(82.09) / 78.69
(
82.30
) / 76.79
(80.65) / 77.10
(81.70 ± 0.70) / 77.43 ± 0.81
For model upload, we choose the best model on development score: 82.56 with a context length of 16.
Comparisons
The following figure shows the results with different context sized (on development dataset):
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