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A Survey on Relation Classification from Unstructured Medical Text

S. Gupta1 , A.K. Manjhvar2

Section:Survey Paper, Product Type: Journal Paper
Volume-5 , Issue-3 , Page no. 114-118, Mar-2017

Online published on Mar 31, 2017

Copyright © S. Gupta, A.K. Manjhvar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Gupta, A.K. Manjhvar, “A Survey on Relation Classification from Unstructured Medical Text,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.114-118, 2017.

MLA Style Citation: S. Gupta, A.K. Manjhvar "A Survey on Relation Classification from Unstructured Medical Text." International Journal of Computer Sciences and Engineering 5.3 (2017): 114-118.

APA Style Citation: S. Gupta, A.K. Manjhvar, (2017). A Survey on Relation Classification from Unstructured Medical Text. International Journal of Computer Sciences and Engineering, 5(3), 114-118.

BibTex Style Citation:
@article{Gupta_2017,
author = {S. Gupta, A.K. Manjhvar},
title = {A Survey on Relation Classification from Unstructured Medical Text},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2017},
volume = {5},
Issue = {3},
month = {3},
year = {2017},
issn = {2347-2693},
pages = {114-118},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1220},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1220
TI - A Survey on Relation Classification from Unstructured Medical Text
T2 - International Journal of Computer Sciences and Engineering
AU - S. Gupta, A.K. Manjhvar
PY - 2017
DA - 2017/03/31
PB - IJCSE, Indore, INDIA
SP - 114-118
IS - 3
VL - 5
SN - 2347-2693
ER -

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Abstract

Medical documents are rich in information and such information can be useful in building many health applications. Since information in medical documents is often unstructured and in nonstandard natural language so it is difficult to collect and present this information in a structured way. Structured information can be present as named-entity in the text, relationship between clinical entities, summary of the text, etc. To get the specific information from the text, many rule based and machine learning techniques are widely used. In this paper, we present several existing techniques for relation classification from unstructured medical text. We focus on rule based approaches, feature based relation classification approaches and convolutional neural network based approach in context of relation classification from unstructured text. We will also discuss semi supervised approaches for the cases where tagged data set is not much available to train the classifier.

Key-Words / Index Term

Data Mining, Relation Classification, Natural Language Processing

References

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