Rule-Based Information Extraction is Dead! Long Live Rule-Based Information Extraction Systems!

TLDR

The rise of Big Data analytics has renewed interest in information extraction, yet a disconnect exists between industry and academia over its perceived benefits and costs, with academia viewing rule‑based IE as lacking research challenges. The authors argue for the importance of rule‑based IE to industry and propose a research agenda to advance its state‑of‑the‑art and bridge academia–industry gaps. The study surveys IE technologies, highlighting the industry–academia disconnect over rule‑based IE, and outlines a research agenda to advance its state‑of‑the‑art.

Abstract

The rise of “Big Data” analytics over unstructured text has led to renewed interest in information extraction (IE). We surveyed the landscape of IE technologies and identified a major disconnect between industry and academia: while rule-based IE dominates the commercial world, it is widely regarded as dead-end technology by the academia. We believe the disconnect stems from the way in which the two communities measure the benefits and costs of IE, as well as academia’s perception that rulebased IE is devoid of research challenges. We make a case for the importance of rule-based IE to industry practitioners. We then lay out a research agenda in advancing the state-of-theart in rule-based IE systems which we believe has the potential to bridge the gap between academic research and industry practice.