Coupling for Coreference Resolution in a Never-ending Learning System
Felipe Quecole, Maísa Cristina Duarte, Estevam Rafael Hruschka Junior · Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais) · 2018
The Never-Ending Language Learning (NELL) is a system that attempts learning to learn from the Web every day, in an autonomous way.Maintaining high precision is the key to keeping the NELL's learning active and improving 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, and simple ensemble methods, 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.