Identifying Semantic Relations in Context: Near-misses and Overlaps
Alla Rozovskaya, Roxana Gîrju · 2015
This paper addresses the problem of semantic relation identification for a set of relations difficult to differen-tiate: near-misses and overlaps. Based on empirical observations on a fairly large dataset of such exam-ples we provide an analysis and a taxonomy of such cases. Using this taxonomy we create various contin-gency sets of relations. These semantic categories are automatically identified by training and testing three state-of-the-art semantic classifiers employing various feature sets. The results show that in order to identify such near-misses and overlaps accurately, a seman-tic relation identification system needs to go beyond the ontological information of the two nouns and rely heavily on contextual and pragmatic knowledge. Keywords lexical semantics; semantic relations; machine learning 1