ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms
and random words that is derived from the already introduced ROOT13 (Santus et
al., 2016). It relies on a Random Forest algorithm and nine unsupervised
corpus-based features. We evaluate it with a 10-fold cross validation on 9,600
pairs, equally distributed among the three classes and involving several
Parts-Of-Speech (i.e. adjectives, nouns and verbs). When all the classes are
present, ROOT9 achieves an F1 score of 90.7%, against a baseline of 57.2%
(vector cosine). When the classification is binary, ROOT9 achieves the
following results against the baseline: hypernyms-co-hyponyms 95.7% vs. 69.8%,
hypernyms-random 91.8% vs. 64.1% and co-hyponyms-random 97.8% vs. 79.4%. In
order to compare the performance with the state-of-the-art, we have also
evaluated ROOT9 in subsets of the Weeds et al. (2014) datasets, proving that it
is in fact competitive. Finally, we investigated whether the system learns the
semantic relation or it simply learns the prototypical hypernyms, as claimed by
Levy et al. (2015). The second possibility seems to be the most likely, even
though ROOT9 can be trained on negative examples (i.e., switched hypernyms) to
drastically reduce this bias.Comment: in LREC 201