21,321 research outputs found
Using Learning to Rank Approach to Promoting Diversity for Biomedical Information Retrieval with Wikipedia
In most of the traditional information retrieval (IR) models, the independent
relevance assumption is taken, which assumes the relevance of a document is
independent of other documents. However, the pitfall of this is the high redundancy
and low diversity of retrieval result. This has been seen in many scenarios, especially
in biomedical IR, where the information need of one query may refer to different
aspects. Promoting diversity in IR takes the relationship between documents into
account. Unlike previous studies, we tackle this problem in the learning to rank
perspective. The main challenges are how to find salient features for biomedical data
and how to integrate dynamic features into the ranking model. To address these
challenges, Wikipedia is used to detect topics of documents for generating diversity
biased features. A combined model is proposed and studied to learn a diversified
ranking result. Experiment results show the proposed method outperforms baseline
models
A neural autoencoder approach for document ranking and query refinement in pharmacogenomic information retrieval
In this study, we investigate learning-to-
rank and query refinement approaches for
information retrieval in the pharmacogenomic domain. The goal is to improve the
information retrieval process of biomedical curators, who manually build knowledge bases for personalized medicine. We
study how to exploit the relationships be-
tween genes, variants, drugs, diseases and
outcomes as features for document ranking and query refinement.
For a supervised approach, we are faced with a
small amount of annotated data and a large
amount of unannotated data. Therefore,
we explore ways to use a neural document
auto-encoder in a semi-supervised approach. We show that a combination of established algorithms, feature-engineering
and a neural auto-encoder model yield
promising results in this setting
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank
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
Using distributional similarity to organise biomedical terminology
We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy
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