A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
Table 5: Official entity discovery performance of our methods on KBP2016 trilingual EDL track.
['LANG', 'NAME P', 'NAME R', 'NAME F1', 'NOMINAL P', 'NOMINAL R', 'NOMINAL F1', 'OVERALL P', 'OVERALL R', 'OVERALL F1']
After fixing some system bugs, we have used both the KBP2015 data and iFLYTEK data to re-train our models for three languages and finally submitted three systems to the final KBP2016 EDL2 evaluation. In our systems, we treat all nominal mentions as special types of named entities and both named and nominal entities are recognized using one model. Here we have broken down the system performance according to different languages and categories of entities (named or nominal). In RUN1, we have submitted our best NER system, achieving about 0.718 in F1 score in the KBP2016 trilingual EDL track. This is a very strong performance among all KBP2016 participating teams. The overall trilingual F1 score is improved to 0.754. It is worth to note that we have obtained a pretty high recall rate, about 0.735, after the system combination because the NER methods used by these two systems are quite complementary.