End-to-end Network for Twitter Geolocation Prediction and Hashing
Table 6: Retrieval MAP performance.
['[BOLD] Bits', '[BOLD] deepgeo', '[BOLD] deepgeo + [BOLD] noise', '[BOLD] deepgeo + [BOLD] loss', '[BOLD] lsh  [BOLD] word2vec', '[BOLD] lsh  [BOLD] deepgeo', '[BOLD] fasthash  [BOLD] word2vec', '[BOLD] fasthash  [BOLD] deepgeo']
Looking at deepgeo systems (column 2–4), we see that adding noise and l helps, although the impact is greater when the bit size is large (300/400 bits). For lsh, which uses no label information, word2vec input produces poor binary code for retrieval. Changing the input to deepgeo improves retrieval considerably, implying that the representation produced by deepgeo captures geolocation information.