AutoEncoder Guided Bootstrapping of Semantic Lexicon

Abstract

Mutual bootstrapping is a commonly used technique for many natural language processing tasks, including semantic lexicon induction. Among many bootstrapping methods, the Basilisk algorithm has led to successful applications through two key iterative steps: scoring context patterns and candidate instances. In this work, we improve Basilisk by modifying its two scoring functions. By incorporating AutoEncoder in the scoring functions of patterns and candidates, we can reduce the bias problems and obtain more balanced results. The experimental results demonstrate that our proposed methods for guiding the bootstrapping of a semantic lexicon with AutoEncoder can boost overall performance.