Abstract

Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand crafted features and domain specific resources which are difficult to collect and tune. For this reason, this paper investigates the effectiveness of contemporary recurrent neural architectures - the Elman and Jordan networks and the bidirectional LSTM with CRF decoding - at performing DNR straight from the text. The experimental results achieved on the authoritative SemEval-2013 Task 9.1 benchmarks show that the bidirectional LSTM-CRF ranks closely to highly-dedicated, hand-crafted systems.

Highlights

  • Pharmacovigilance (PV) is defined by the World Health Organization as the science and activities concerned with the detection, assessment, understanding and prevention of adverse effects of drugs or any other drug-related problems

  • This paper explores the performance of contemporary recurrent neural networks (RNNs) at providing endto-end Drug name recognition (DNR) straight from text, without any manual feature engineering stage

  • The main goal of this paper is to provide the first performance investigation of popular RNNs such as the Elman and Jordan networks and the bidirectional long short-term memory (LSTM)-conditional random field (CRF) over DNR tasks

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Summary

Introduction

Pharmacovigilance (PV) is defined by the World Health Organization as the science and activities concerned with the detection, assessment, understanding and prevention of adverse effects of drugs or any other drug-related problems. Dictionarybased systems identify drug names by matching text chunks against drug dictionaries. These systems typically achieve high precision, but suffer from low recall (i.e., they miss a significant number of mentions) due to spelling errors or drug name variants not present in the dictionaries (Liu et al, 2015a). To the design of rule-based systems, this task requires much expert knowledge, is typically challenging and time-consuming, and has a major impact on the final accuracy. For this reason, this paper explores the performance of contemporary recurrent neural networks (RNNs) at providing endto-end DNR straight from text, without any manual feature engineering stage. The experimental results over the SemEval-2013 Task 9.1 benchmarks show an interesting accuracy from the LSTM-CRF

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