Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks
Table 3: Comparison of using only topic information derived from the document and target article, only information derived from the mention itself and the target entity title, and the full set of information (six features, as shown in Figure 1). Neither the finest nor coarsest convolutional context can give the performance of the complete set. Numbers are reported on a development set.
['[EMPTY]', 'ACE', 'CoNLL', 'WP']
One question we might ask is how much we gain by having multiple convolutions on the source and target side. These represent two extremes of the system: consuming the maximum amount of context, which might give the most robust representation of topic semantics, and consuming the minimum amount of context, which gives the most focused representation of topics semantics (and which more generally might allow the system to directly memorize train-test pairs observed in training). However, neither performs as well as the combination of all CNN features, showing that the different granularities capture complementary aspects of the entity linking task.