33,055 research outputs found
Few-shot classification in Named Entity Recognition Task
For many natural language processing (NLP) tasks the amount of annotated data
is limited. This urges a need to apply semi-supervised learning techniques,
such as transfer learning or meta-learning. In this work we tackle Named Entity
Recognition (NER) task using Prototypical Network - a metric learning
technique. It learns intermediate representations of words which cluster well
into named entity classes. This property of the model allows classifying words
with extremely limited number of training examples, and can potentially be used
as a zero-shot learning method. By coupling this technique with transfer
learning we achieve well-performing classifiers trained on only 20 instances of
a target class.Comment: In proceedings of the 34th ACM/SIGAPP Symposium on Applied Computin
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks
Neural networks (NNs) have become the state of the art in many machine
learning applications, especially in image and sound processing [1]. The same,
although to a lesser extent [2,3], could be said in natural language processing
(NLP) tasks, such as named entity recognition. However, the success of NNs
remains dependent on the availability of large labelled datasets, which is a
significant hurdle in many important applications. One such case are electronic
health records (EHRs), which are arguably the largest source of medical data,
most of which lies hidden in natural text [4,5]. Data access is difficult due
to data privacy concerns, and therefore annotated datasets are scarce. With
scarce data, NNs will likely not be able to extract this hidden information
with practical accuracy. In our study, we develop an approach that solves these
problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009
Medical Extraction Challenge [6], 4.3 above the architecture that won the
competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on
extracting relationships between medical terms. To reach this state-of-the-art
accuracy, our approach applies transfer learning to leverage on datasets
annotated for other I2B2 tasks, and designs and trains embeddings that
specially benefit from such transfer.Comment: 11 pages, 4 figures, 8 table
W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition
Contrastive learning has become a popular solution for few-shot Name Entity
Recognization (NER). The conventional configuration strives to reduce the
distance between tokens with the same labels and increase the distance between
tokens with different labels. The effect of this setup may, however, in the
medical domain, there are a lot of entities annotated as OUTSIDE (O), and they
are undesirably pushed apart to other entities that are not labeled as OUTSIDE
(O) by the current contrastive learning method end up with a noisy prototype
for the semantic representation of the label, though there are many OUTSIDE (O)
labeled entities are relevant to the labeled entities. To address this
challenge, we propose a novel method named Weighted Prototypical Contrastive
Learning for Medical Few Shot Named Entity Recognization (W-PROCER). Our
approach primarily revolves around constructing the prototype-based contractive
loss and weighting network. These components play a crucial role in assisting
the model in differentiating the negative samples from OUTSIDE (O) tokens and
enhancing the discrimination ability of contrastive learning. Experimental
results show that our proposed W-PROCER framework significantly outperforms the
strong baselines on the three medical benchmark datasets
Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges
In the domain of Natural Language Processing (NLP), Named Entity Recognition
(NER) stands out as a pivotal mechanism for extracting structured insights from
unstructured text. This manuscript offers an exhaustive exploration into the
evolving landscape of NER methodologies, blending foundational principles with
contemporary AI advancements. Beginning with the rudimentary concepts of NER,
the study spans a spectrum of techniques from traditional rule-based strategies
to the contemporary marvels of transformer architectures, particularly
highlighting integrations such as BERT with LSTM and CNN. The narrative
accentuates domain-specific NER models, tailored for intricate areas like
finance, legal, and healthcare, emphasizing their specialized adaptability.
Additionally, the research delves into cutting-edge paradigms including
reinforcement learning, innovative constructs like E-NER, and the interplay of
Optical Character Recognition (OCR) in augmenting NER capabilities. Grounding
its insights in practical realms, the paper sheds light on the indispensable
role of NER in sectors like finance and biomedicine, addressing the unique
challenges they present. The conclusion outlines open challenges and avenues,
marking this work as a comprehensive guide for those delving into NER research
and applications
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