SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble

TL;DR

SetExpan is a novel framework for corpus-based set expansion that improves entity discovery by selecting clean context features and using an ensemble ranking method, outperforming previous approaches.

Contribution

It introduces a new context feature selection and rank ensemble approach to enhance set expansion accuracy in noisy corpora.

Findings

SetExpan outperforms state-of-the-art methods in mean average precision.

The framework effectively reduces noise from context features.

Experiments on three datasets demonstrate robustness and improved accuracy.

Abstract

Corpus-based set expansion (i.e., finding the "complete" set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search. To discover new entities in an expanded set, previous approaches either make one-time entity ranking based on distributional similarity, or resort to iterative pattern-based bootstrapping. The core challenge for these methods is how to deal with noisy context features derived from free-text corpora, which may lead to entity intrusion and semantic drifting. In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features. Experiments on three datasets show that SetExpan is robust and outperforms previous state-of-the-art methods in terms of mean average precision.