Open Information Extraction (Open IE) systems aim to obtain relation tuples
with highly scalable extraction in portable across domain by identifying a
variety of relation phrases and their arguments in arbitrary sentences. The
first generation of Open IE learns linear chain models based on unlexicalized
features such as Part-of-Speech (POS) or shallow tags to label the intermediate
words between pair of potential arguments for identifying extractable
relations. Open IE currently is developed in the second generation that is able
to extract instances of the most frequently observed relation types such as
Verb, Noun and Prep, Verb and Prep, and Infinitive with deep linguistic
analysis. They expose simple yet principled ways in which verbs express
relationships in linguistics such as verb phrase-based extraction or
clause-based extraction. They obtain a significantly higher performance over
previous systems in the first generation. In this paper, we describe an
overview of two Open IE generations including strengths, weaknesses and
application areas.

本文概述了两代 Open IE 系统，包括其优点、弱点和应用范围。第二代系统在深度语言学分析下，通过动词短语或从句提取最常见的关系类型，并获得比第一代系统更高的性能。