 |
A Lightweight Neural Model for Biomedical Entity Linking
Abstract : Biomedical entity linking aims to map biomedical mentions,
such as diseases and drugs, to standard entities in a given
knowledge base. The specific challenge in this context is
that the same biomedical entity can have a wide range of
names, including synonyms, morphological variations, and
names with different word orderings. Recently, BERT-based
methods have advanced the state-of-the-art by allowing for
rich representations of word sequences. However, they often
have hundreds of millions of parameters and require
heavy computing resources, which limits their applications
in resource-limited scenarios. Here, we propose a lightweight
neural method for biomedical entity linking, which needs just
a fraction of the parameters of a BERT model and much less
computing resources. Our method uses a simple alignment
layer with attention mechanisms to capture the variations
between mention and entity names. Yet, we show that our
model is competitive with previous work on standard evaluation
benchmarks.
Keywords :
Biomedical Entity Linking
https://hal.archives-ouvertes.fr/hal-03086044
Contributor : Lihu Chen <>
Submitted on : Tuesday, December 22, 2020 - 11:30:36 AM
Last modification on : Thursday, January 14, 2021 - 3:18:58 AM
Contributor : Lihu Chen <>
Submitted on : Tuesday, December 22, 2020 - 11:30:36 AM
Last modification on : Thursday, January 14, 2021 - 3:18:58 AM
File
8013.LihuC.pdf
Files produced by the author(s)