Corpora for Learning the Mutual Relation between Semantic Relatedness and Textual Entailment
Vo Ngoc-Phuoc-An, Octavian Popescu
In this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE)
judgments. In building this corpus we aimed at discovering, if any, the relationship between these two tasks for the mutual benefit of
resolving one of them by relying on the insights gained from the other. We considered a corpora already annotated with TE judgments
and we proceed to the manual annotation with SR scores. The RTE 1-4 corpora used in the PASCAL competition fit our need. The
annotators worked independently of one each other and they did not have access to the TE judgment during annotation. The intuition
that the two annotations are correlated received major support from this experiment and this finding led to a system that uses this
information to revise the initial estimates of SR scores. As semantic relatedness is one of the most general and difficult task in natural
language processing we expect that future systems will combine different sources of information in order to solve it. Our work suggests
that textual entailment plays a quantifiable role in addressing it.
LREC, Portoroz, Slovenia, May 23-28, 2016.