Research on Question Classification Methods in the Medical Field
- URL: http://arxiv.org/abs/2202.00298v1
- Date: Tue, 1 Feb 2022 09:58:30 GMT
- Title: Research on Question Classification Methods in the Medical Field
- Authors: Jinzhang Liu
- Abstract summary: This paper presents a data set for question classification in the medical field.
The experimental results show that the proposed method can effectively improve the performance of question classification.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Question classification is one of the important links in the research of
question and answering system. The existing question classification models are
more trained on public data sets. At present, there is a lack of question
classification data sets in specific fields, especially in the medical field.
To make up for this gap, this paper presents a data set for question
classification in the medical field. Moreover, this paper proposes a
multi-dimensional extraction of the characteristics of the question by
combining multiple neural network models, and proposes a question
classification model based on multi-dimensional feature extraction. The
experimental results show that the proposed method can effectively improve the
performance of question classification.
Related papers
- Large Language Models for Multi-Choice Question Classification of Medical Subjects [0.2020207586732771]
We train deep neural networks for multi-class classification of questions into the inferred medical subjects.
We show the capability of AI and LLMs in particular for multi-classification tasks in the Healthcare domain.
arXiv Detail & Related papers (2024-03-21T17:36:08Z) - UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
We propose a universal cell nucleus classification framework (UniCell)
It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains.
In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
arXiv Detail & Related papers (2024-02-20T11:50:27Z) - Multi-task Explainable Skin Lesion Classification [54.76511683427566]
We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data.
The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
arXiv Detail & Related papers (2023-10-11T05:49:47Z) - Hyperspectral Image Analysis with Subspace Learning-based One-Class
Classification [18.786429304405097]
Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification.
In this study, we investigate recently proposed subspace learning methods for one-class classification (OCC)
In this way, there is no separate dimensionality reduction or feature selection procedure needed in the proposed classification framework.
Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data.
arXiv Detail & Related papers (2023-04-19T15:17:05Z) - Multi-class versus One-class classifier in spontaneous speech analysis
oriented to Alzheimer Disease diagnosis [58.720142291102135]
The aim of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from speech signal.
The use of information about outlier and Fractal Dimension features improves the system performance.
arXiv Detail & Related papers (2022-03-21T09:57:20Z) - Learning Debiased and Disentangled Representations for Semantic
Segmentation [52.35766945827972]
We propose a model-agnostic and training scheme for semantic segmentation.
By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes.
Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
arXiv Detail & Related papers (2021-10-31T16:15:09Z) - Discriminative Attribution from Counterfactuals [64.94009515033984]
We present a method for neural network interpretability by combining feature attribution with counterfactual explanations.
We show that this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner.
arXiv Detail & Related papers (2021-09-28T00:53:34Z) - A Framework for Multi-View Classification of Features [6.660458629649826]
In solving the data classification problems, when the feature set is too large, typical approaches will not be able to solve the problem.
In this research, an innovative framework for multi-view ensemble classification, inspired by the problem of object recognition in the multiple views theory of humans, is proposed.
arXiv Detail & Related papers (2021-08-02T16:27:43Z) - Enhancing Fine-Grained Classification for Low Resolution Images [97.82441158440527]
Low resolution images suffer from the inherent challenge of limited information content and the absence of fine details useful for sub-category classification.
This research proposes a novel attribute-assisted loss, which utilizes ancillary information to learn discriminative features for classification.
The proposed loss function enables a model to learn class-specific discriminative features, while incorporating attribute-level separability.
arXiv Detail & Related papers (2021-05-01T13:19:02Z) - Evaluating Nonlinear Decision Trees for Binary Classification Tasks with
Other Existing Methods [8.870380386952993]
Classification of datasets into two or more distinct classes is an important machine learning task.
Many methods are able to classify binary classification tasks with a very high accuracy on test data, but cannot provide any easily interpretable explanation.
We highlight and evaluate a recently proposed nonlinear decision tree approach with a number of commonly used classification methods on a number of datasets.
arXiv Detail & Related papers (2020-08-25T00:00:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.