Strong Baselines for Simple Question Answering overKnowledge Graphs with and without Neural Networks
Table 1: Results for entity linking on the validation set, given the underlying entity detection model.
['[BOLD] R@ [ITALIC] N', '[BOLD] BiLSTM', '[BOLD] CRF']
For entity detection, on the validation set, the BiLSTM (which outperforms the BiGRU) achieves 93.1 [92.8 93.4] F1, compared to the CRF at 90.2. We see that entity linking using the CRF achieves comparable accuracy, even though the CRF performs slightly worse on entity detection alone; entity linking appears to be the bottleneck. Error analysis shows that there is a long tail of highly-ambiguous entities—that is, entities in the knowledge graph that have the same label—and that even at depth 50, we are unable to identify the correct entity (MID) more than 10% of the time.