  Relation Extraction is an important task in Information Extraction which
deals with identifying semantic relations between entity mentions.
Traditionally, relation extraction is carried out after entity extraction in a
"pipeline" fashion, so that relation extraction only focuses on determining
whether any semantic relation exists between a pair of extracted entity
mentions. This leads to propagation of errors from entity extraction stage to
relation extraction stage. Also, entity extraction is carried out without any
knowledge about the relations. Hence, it was observed that jointly performing
entity and relation extraction is beneficial for both the tasks. In this paper,
we survey various techniques for jointly extracting entities and relations. We
categorize techniques based on the approach they adopt for joint extraction,
i.e. whether they employ joint inference or joint modelling or both. We further
describe some representative techniques for joint inference and joint
modelling. We also describe two standard datasets, evaluation techniques and
performance of the joint extraction approaches on these datasets. We present a
brief analysis of application of a general domain joint extraction approach to
a Biomedical dataset. This survey is useful for researchers as well as
practitioners in the field of Information Extraction, by covering a broad
landscape of joint extraction techniques.
