  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.
