Techniques for Jointly Extracting Entities and Relations: A Survey

by   Sachin Pawar, et al.

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.



There are no comments yet.


page 1

page 2

page 3

page 4


End-to-End Relation Extraction using Markov Logic Networks

The task of end-to-end relation extraction consists of two sub-tasks: i)...

TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking

Extracting entities and relations from unstructured text has attracted i...

FoodChem: A food-chemical relation extraction model

In this paper, we present FoodChem, a new Relation Extraction (RE) model...

Adversarial training for multi-context joint entity and relation extraction

Adversarial training (AT) is a regularization method that can be used to...

Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision

Understanding the meaning of text often involves reasoning about entitie...

Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification

We introduce globally normalized convolutional neural networks for joint...

A logic-based relational learning approach to relation extraction: The OntoILPER system

Relation Extraction (RE), the task of detecting and characterizing seman...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.