  Recognizing various semantic relations between terms is beneficial for many
NLP tasks. While path-based and distributional information sources are
considered complementary for this task, the superior results the latter showed
recently suggested that the former's contribution might have become obsolete.
We follow the recent success of an integrated neural method for hypernymy
detection (Shwartz et al., 2016) and extend it to recognize multiple relations.
The empirical results show that this method is effective in the multiclass
setting as well. We further show that the path-based information source always
contributes to the classification, and analyze the cases in which it mostly
complements the distributional information.
