Path-based vs. Distributional Information in Recognizing Lexical Semantic Relations
This work addresses the need for improved semantic relation recognition in NLP, but it is incremental as it builds on prior methods without introducing major new paradigms.
The paper tackled the problem of recognizing multiple lexical semantic relations in NLP by extending an integrated neural method from hypernymy detection to a multiclass setting, showing it remains effective and that path-based information consistently contributes and complements distributional information.
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.