A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
Table 4: Entity discovery performance (English only) in KBP2016 EDL1 evaluation window is shown as a comparison of three models trained by different combinations of training data sets.
['training data', 'P', 'R', '[ITALIC] F1']
In our first set of experiments, we investigate the effect of using different training data sets on the final entity discovery performance. Different training runs are conducted on different combinations of the aforementioned data sources. The first system, using only the KBP2015 data to train the model, has achieved 0.693 in F1 score in the official KBP2016 English evaluation data. After adding the weakly labelled data, WIKI, we can see the entity discovery performance is improved to 0.707 in F1 score. Finally, we can see that it yields the best performance by using the KBP2015 data and the iFLYTEK in-house data sets to train our models, giving 0.731 in F1 score.