TELKOMNIKA Telecommunication, Computing, Electronics and Control
A novel deep learning architecture for drug named entity recognition

TELKOMNIKA Telecommunication, Computing, Electronics and Control
A novel deep learning architecture for drug named entity recognition

Drug named entity recognition
Natural language processing
Residual LSTM
Sentence level embedding
Stacked Bi-LSTM

Drug named entity recognition (DNER) becomes the prerequisite of other
medical relation extraction systems. Existing approaches to automatically
recognize drug names includes rule-based, machine learning (ML) and deep

learning (DL) techniques. DL techniques have been verified to be the state-
of-the-art as it is independent of handcrafted features. The previous DL

methods based on word embedding input representation uses the same vector
representation for an entity irrespective of its context in different sentences
and hence could not capture the context properly. Also, identification of the
n-gram entity is a challenge. In this paper, a novel architecture is proposed
that includes a sentence embedding layer that works on the entire sentence to
efficiently capture the context of an entity. A hybrid model that comprises a
stacked bidirectional long short-term memory (Bi-LSTM) with residual
LSTM has been designed to overcome the limitations and to upgrade the
performance of the model. We have contrasted the achievement of our
proposed approach with other DNER models and the percentage of
improvements of the proposed model over LSTM-conditional random field
(CRF), LIU and WBI with respect to micro-average F1-score are 11.17, 8.8
and 17.64 respectively. The proposed model has also shown promising result
in recognizing 2- and 3-gram entities.

T. Mathu, Kumudha Raimond

Oct 18, 2021

peri irawan

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