Document Type

Conference Paper

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Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences

Publication Details

Text Analysis Conference (TAC) November 13-14, 2018

Drug-Drug Interaction Extraction from Drug Labels (DDI)
The purpose of the DDI track is to test various natural language processing (NLP) approaches for their information extraction (IE) performance on drug-drug interactions in Structured Product Labeling (SPL) documents.
Track coordinator: Dina Demner-Fushman (ddemner@mail.nih.gov)
Home page: https://bionlp.nlm.nih.gov/tac2018druginteractions/
Mailing list: tac-adr@googlegroups.com

Abstract

The DDI track of TAC-2018 challenge addresses the problem of an information retrieval of drug-drug interactions on structured product labeling documents with discontinuous and overlapping entities. In this paper, we present our participation for event extraction subtask (Task 1). We used a supervised long-short-term memory (LSTM) network with conditional random fields decoding (LSTM-CRF) approach with an automatic exploring of words and characters features. Additional dependency-based information was integrated into word embeddings to allow better word representation. Our system performed with above median score.

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