DLocRL: Deep Pipeline for Twitter Location Recognition and Linking
Table 2. Effect of Multi-attention Mechanism for Linking.
['Attention', 'Location Linking  [ITALIC] Pr', 'Location Linking  [ITALIC] Re', 'Location Linking  [ITALIC] F1']
We conduct experiments to verify the effectiveness of the multi-attention mechanism. Note that the single attention approaches can only slightly improve the performance because they have “prejudice” which considers either behavioral information or semantic information. By “fusing” this two information, DLocRL eliminates the prejudice so it can effectively filter out noisy information. With the multi-attention mechanism, DLocRL improves the performance of linking module by 4.25% on precision, 4.26% on recall, and 4.26% on F1, compared with the baseline.