Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to
  Rank
Zainab Awan Tim Kahlke Peter Ralph Paul Kennedy
Motivation: Biomedical named-entity normalization involves connecting
biomedical entities with distinct database identifiers in order to facilitate
data integration across various fields of biology. Existing systems for
biomedical named entity normalization heavily rely on dictionaries, manually
created rules, and high-quality representative features such as lexical or
morphological characteristics. However, recent research has investigated the
use of neural network-based models to reduce dependence on dictionaries,
manually crafted rules, and features. Despite these advancements, the
performance of these models is still limited due to the lack of sufficiently
large training datasets. These models have a tendency to overfit small training
corpora and exhibit poor generalization when faced with previously unseen
entities, necessitating the redesign of rules and features. Contribution: We
present a novel deep learning approach for named entity normalization, treating
it as a pair-wise learning to rank problem. Our method utilizes the widely-used
information retrieval algorithm Best Matching 25 to generate candidate
concepts, followed by the application of bi-directional encoder representation
from the encoder (BERT) to re-rank the candidate list. Notably, our approach
eliminates the need for feature-engineering or rule creation. We conduct
experiments on species entity types and evaluate our method against
state-of-the-art techniques using LINNAEUS and S800 biomedical corpora. Our
proposed approach surpasses existing methods in linking entities to the NCBI
taxonomy. To the best of our knowledge, there is no existing neural
network-based approach for species normalization in the literature.