Coupling for Coreference Resolution in a Never-ending Learning System

Abstract

The Never-Ending Language Learning (NELL) is a system that attempts learning to learn from the Web every day, in an autonomously way. Keeping a high precision is the key to keep the NELL’s learning active and better day-by-day. One of the challenges for NELL system is to properly identify different noun phrases that denote the same concept in order to maintain the cohesion of the knowledge base. This article investigates the coupling as an approach for improving coreference resolution on NELL. For that, several coupled algorithms considering semantic and morphologic features were compared with results previously obtained with no use of coupling. The results presented in this article confirm empirically that coupling strategy is a useful and good approach to achieve better coverage and accuracy in NELL’s knowledge base.