30,476 research outputs found
Knowledge-Rich Self-Supervision for Biomedical Entity Linking
Entity linking faces significant challenges such as prolific variations and
prevalent ambiguities, especially in high-value domains with myriad entities.
Standard classification approaches suffer from the annotation bottleneck and
cannot effectively handle unseen entities. Zero-shot entity linking has emerged
as a promising direction for generalizing to new entities, but it still
requires example gold entity mentions during training and canonical
descriptions for all entities, both of which are rarely available outside of
Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision () for biomedical entity linking, by leveraging readily available domain
knowledge. In training, it generates self-supervised mention examples on
unlabeled text using a domain ontology and trains a contextual encoder using
contrastive learning. For inference, it samples self-supervised mentions as
prototypes for each entity and conducts linking by mapping the test mention to
the most similar prototype. Our approach can easily incorporate entity
descriptions and gold mention labels if available. We conducted extensive
experiments on seven standard datasets spanning biomedical literature and
clinical notes. Without using any labeled information, our method produces , a universal entity linker for four million UMLS entities that
attains new state of the art, outperforming prior self-supervised methods by as
much as 20 absolute points in accuracy
BELB: a Biomedical Entity Linking Benchmark
Biomedical entity linking (BEL) is the task of grounding entity mentions to a
knowledge base. It plays a vital role in information extraction pipelines for
the life sciences literature. We review recent work in the field and find that,
as the task is absent from existing benchmarks for biomedical text mining,
different studies adopt different experimental setups making comparisons based
on published numbers problematic. Furthermore, neural systems are tested
primarily on instances linked to the broad coverage knowledge base UMLS,
leaving their performance to more specialized ones, e.g. genes or variants,
understudied. We therefore developed BELB, a Biomedical Entity Linking
Benchmark, providing access in a unified format to 11 corpora linked to 7
knowledge bases and spanning six entity types: gene, disease, chemical,
species, cell line and variant. BELB greatly reduces preprocessing overhead in
testing BEL systems on multiple corpora offering a standardized testbed for
reproducible experiments. Using BELB we perform an extensive evaluation of six
rule-based entity-specific systems and three recent neural approaches
leveraging pre-trained language models. Our results reveal a mixed picture
showing that neural approaches fail to perform consistently across entity
types, highlighting the need of further studies towards entity-agnostic models
Incorporating Ontological Information in Biomedical Entity Linking of Phrases in Clinical Text
Biomedical Entity Linking (BEL) is the task of mapping spans of text within biomedical documents to normalized, unique identifiers within an ontology. Translational application of BEL on clinical notes has enormous potential for augmenting discretely captured data in electronic health records, but the existing paradigm for evaluating BEL systems developed in academia is not well aligned with real-world use cases. In this work, we demonstrate a proof of concept for incorporating ontological similarity into the training and evaluation of BEL systems to begin to rectify this misalignment. This thesis has two primary components: 1) a comprehensive literature review and 2) a methodology section to propose novel BEL techniques to contribute to scientific progress in the field. In the literature review component, I survey the progression of BEL from its inception in the late 80s to present day state of the art systems, provide a comprehensive list of datasets available for training BEL systems, reference shared tasks focused on BEL, and outline the technical components that vii comprise BEL systems. In the methodology component, I describe my experiments incorporating ontological information into training a BERT encoder for entity linking
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
Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets
Objectives
Biomedical natural language processing tools are increasingly being applied for broad-coverage information extraction—extracting medical information of all types in a scientific document or a clinical note. In such broad-coverage settings, linking mentions of medical concepts to standardized vocabularies requires choosing the best candidate concepts from large inventories covering dozens of types. This study presents a novel semantic type prediction module for biomedical NLP pipelines and two automatically-constructed, large-scale datasets with broad coverage of semantic types.
Methods
We experiment with five off-the-shelf biomedical NLP toolkits on four benchmark datasets for medical information extraction from scientific literature and clinical notes. All toolkits adopt a staged approach of mention detection followed by two stages of medical entity linking: (1) generating a list of candidate concepts, and (2) picking the best concept among them. We introduce a semantic type prediction module to alleviate the problem of overgeneration of candidate concepts by filtering out irrelevant candidate concepts based on the predicted semantic type of a mention. We present MedType, a fully modular semantic type prediction model which we integrate into the existing NLP toolkits. To address the dearth of broad-coverage training data for medical information extraction, we further present WikiMed and PubMedDS, two large-scale datasets for medical entity linking.
Results
Semantic type filtering improves medical entity linking performance across all toolkits and datasets, often by several percentage points of F-1. Further, pretraining MedType on our novel datasets achieves state-of-the-art performance for semantic type prediction in biomedical text.
Conclusions
Semantic type prediction is a key part of building accurate NLP pipelines for broad-coverage information extraction from biomedical text. We make our source code and novel datasets publicly available to foster reproducible research
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
The SourceData-NLP dataset: integrating curation into scientific publishing for training large language models
Introduction: The scientific publishing landscape is expanding rapidly,
creating challenges for researchers to stay up-to-date with the evolution of
the literature. Natural Language Processing (NLP) has emerged as a potent
approach to automating knowledge extraction from this vast amount of
publications and preprints. Tasks such as Named-Entity Recognition (NER) and
Named-Entity Linking (NEL), in conjunction with context-dependent semantic
interpretation, offer promising and complementary approaches to extracting
structured information and revealing key concepts.
Results: We present the SourceData-NLP dataset produced through the routine
curation of papers during the publication process. A unique feature of this
dataset is its emphasis on the annotation of bioentities in figure legends. We
annotate eight classes of biomedical entities (small molecules, gene products,
subcellular components, cell lines, cell types, tissues, organisms, and
diseases), their role in the experimental design, and the nature of the
experimental method as an additional class. SourceData-NLP contains more than
620,000 annotated biomedical entities, curated from 18,689 figures in 3,223
papers in molecular and cell biology. We illustrate the dataset's usefulness by
assessing BioLinkBERT and PubmedBERT, two transformers-based models, fine-tuned
on the SourceData-NLP dataset for NER. We also introduce a novel
context-dependent semantic task that infers whether an entity is the target of
a controlled intervention or the object of measurement.
Conclusions: SourceData-NLP's scale highlights the value of integrating
curation into publishing. Models trained with SourceData-NLP will furthermore
enable the development of tools able to extract causal hypotheses from the
literature and assemble them into knowledge graphs
- …