DeepVar: An end-to-end deep learning approach for genomic variant recognition in biomedical literature

Document Type

Conference Proceeding

Publication Date

1-1-2020

Abstract

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any handcrafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.

Identifier

85105980029 (Scopus)

ISBN

[9781577358350]

Publication Title

Aaai 2020 34th Aaai Conference on Artificial Intelligence

External Full Text Location

https://doi.org/10.1609/aaai.v34i01.5399

First Page

598

Last Page

605

This document is currently not available here.

Share

COinS