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A Survey on Relation Classification from Unstructured Medical Text
S. Gupta, A.K. Manjhvar

Published in: International Journal of Computer Sciences and Engineering
Volume- 5, Issue-3, pp.114-118, Mar 2017
DPI :-> 16.10022.IJCSE.2017.V5I3.114118.1432

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

How to cite this article
S. Gupta, A.K. Manjhvar , “A Survey on Relation Classification from Unstructured Medical Text”, International Journal of Computer Sciences and Engineering, 5, Issue-3, pp.114-118, Mar 2017. DPI:16.10022.IJCSE.V5.I3.1432