Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks
Table 4: Results of the full model (sparse and convolutional features) comparing word vectors derived from Google News vs. Wikipedia on development sets for each corpus.
['[EMPTY]', 'ACE', 'CoNLL', 'WP']
We also explored two different sources of embedding vectors for the convolutions. Further investigation revealed that the Google News vectors had much higher out-of-vocabulary rates. We do not fine-tune word vectors during training of our model, as that was not found to improve performance.