21,609 research outputs found
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
Although deep learning techniques have shown significant achievements, they
frequently depend on extensive amounts of hand-labeled data and tend to perform
inadequately in few-shot scenarios. The objective of this study is to devise a
strategy that can improve the model's capability to recognize biomedical
entities in scenarios of few-shot learning. By redefining biomedical named
entity recognition (BioNER) as a machine reading comprehension (MRC) problem,
we propose a demonstration-based learning method to address few-shot BioNER,
which involves constructing appropriate task demonstrations. In assessing our
proposed method, we compared the proposed method with existing advanced methods
using six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical,
BC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA. We examined the models'
efficacy by reporting F1 scores from both the 25-shot and 50-shot learning
experiments. In 25-shot learning, we observed 1.1% improvements in the average
F1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%,
50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further
improved the average F1 scores by 1.0% compared to the baseline method,
reaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively. We
reported that in the realm of few-shot learning BioNER, MRC-based language
models are much more proficient in recognizing biomedical entities compared to
the sequence labeling approach. Furthermore, our MRC-language models can
compete successfully with fully-supervised learning methodologies that rely
heavily on the availability of abundant annotated data. These results highlight
possible pathways for future advancements in few-shot BioNER methodologies
Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts
Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. This work is difficult due to the complexity of scientific language and the abundance of available material in the biomedical sector. Using domain-specific embedding and Bi-LSTM, we propose a novel NER model that employs deep learning approaches to improve the performance of NER on scientific publications. Our model gets 98% F1-score on a curated data-set of Covid-related scientific publications published in multiple web of science and pubmed indexed journals, significantly outperforming previous approaches deployed on the same data-set. Our findings illustrate the efficacy of our approach in reliably recognizing and classifying named entities (drug and disease) in scientific literature, opening the way for future developments in biomedical text mining
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
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