A framework for Entailed Relation Recognition

Abstract

We define the problem of recognizing entailed relations - given an open set of relations, find all occurrences of the relations of interest in a given document set - and pose it as a challenge to scalable information extraction and retrieval. Existing approaches to relation recognition do not address well problems with an open set of relations and a need for high recall: supervised methods are not easily scaled, while unsupervised and semi-supervised methods address a limited aspect of the problem, as they are restricted to frequent, explicit, highly localized patterns. We argue that textual entailment (TE) is necessary to solve such problems, propose a scalable TE architecture, and provide preliminary results on an Entailed Relation Recognition task. © 2009 ACL and AFNLP.