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Table 10: Top five hashtags from four different events of CLEF 2017 lab microblog dataset.
Festival 1: Anna Calvi,
charrues
Festival 2: La Piccola Fa-
milia, avignon
Festival 3: Suitable for par-
ties, transmusicales
Festival 4: Vanishing Point,
edinburgh
#vieillescharrues2015,
#annacalvi, #charrues,
#labelcharrues, #vieillechar-
rues2015
#piccolafamilia, #lapic-
colafamilia, #lafamilia,
#festivaldelafamilia, #frente-
nacionalxlafamilia
#transmusicales, #transmu-
sicales2015, #eventosmu-
sicales, #noticiasmusicales,
#rencontrestransmusicales
#vanishingpoint, #edinburgh-
festivalfringe, #thedestroyed-
room, #edinburghfringe2016,
##edinburg
Table 11: NDCG values obtained on four categories with one of the features removed.
Rank K AlleqW AlldiffW All-{Bigrams} All-{Trigrams} All-{Subsequence} All-{Frequency}
5 0.865 0.926 0.846 0.853 0.888 0.869
10 0.816 0.878 0.811 0.815 0.841 0.869
15 0.804 0.874 0.785 0.789 0.811 0.866
20 0.811 0.869 0.782 0.781 0.810 0.865
25 0.812 0.876 0.793 0.793 0.819 0.870
30 0.832 0.883 0.810 0.809 0.833 0.874
35 0.848 0.894 0.829 0.832 0.846 0.887
40 0.868 0.908 0.849 0.854 0.873 0.903
45 0.896 0.927 0.875 0.880 0.893 0.920
50 0.916 0.949 0.906 0.906 0.922 0.941
identify additional features for hevent, hashtagi pairs.
Also, we want to evaluate the proposed method’s per-
formance on other categories. Moreover, we would
like to see whether the proposed strategy is able to
retrieve hashtags for individual events which are part
of large-scale events (e.g., Rio Olympics, World Cup)
that are agglomerate of various individual events. It
would be an interesting work to use the proposed
method to retrieve relevant tweets for an event and
evaluate the quality of retrieved tweets.
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