CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale
Multi-Label Text Classification
- URL: http://arxiv.org/abs/2109.04853v1
- Date: Fri, 10 Sep 2021 13:09:12 GMT
- Title: CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale
Multi-Label Text Classification
- Authors: Mat\'u\v{s} Falis, Hang Dong, Alexandra Birch, Beatrice Alex
- Abstract summary: We argue for hierarchical evaluation of the predictions of neural LMTC models.
We describe a structural issue in the representation of the structured label space in prior art.
We propose a set of metrics for hierarchical evaluation using the depth-based representation.
- Score: 70.554573538777
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large-Scale Multi-Label Text Classification (LMTC) includes tasks with
hierarchical label spaces, such as automatic assignment of ICD-9 codes to
discharge summaries. Performance of models in prior art is evaluated with
standard precision, recall, and F1 measures without regard for the rich
hierarchical structure. In this work we argue for hierarchical evaluation of
the predictions of neural LMTC models. With the example of the ICD-9 ontology
we describe a structural issue in the representation of the structured label
space in prior art, and propose an alternative representation based on the
depth of the ontology. We propose a set of metrics for hierarchical evaluation
using the depth-based representation. We compare the evaluation scores from the
proposed metrics with previously used metrics on prior art LMTC models for
ICD-9 coding in MIMIC-III. We also propose further avenues of research
involving the proposed ontological representation.
Related papers
- Domain Adaptive Nuclei Instance Segmentation and Classification via
Category-aware Feature Alignment and Pseudo-labelling [65.40672505658213]
We propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification.
Our approach outperforms state-of-the-art UDA methods with a remarkable margin.
arXiv Detail & Related papers (2022-07-04T07:05:06Z) - Hierarchical Label-wise Attention Transformer Model for Explainable ICD
Coding [10.387366211090734]
We propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents.
We evaluate HiLAT using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III database.
Visualisations of attention weights present a potential explainability tool for checking the face validity of ICD code predictions.
arXiv Detail & Related papers (2022-04-22T14:12:22Z) - HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification [7.176984223240199]
Hierarchical multi-label text classification (HMTC) with higher accuracy over large sets of closely related categories has become a challenging problem.
We present a hierarchical and fine-tuning approach based on the Ordered Neural LSTM neural network, abbreviated as HFT-ONLSTM, for more accurate level-by-level HMTC.
arXiv Detail & Related papers (2022-04-18T00:57:46Z) - Semantic Representation and Dependency Learning for Multi-Label Image
Recognition [76.52120002993728]
We propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category.
Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model.
We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions.
arXiv Detail & Related papers (2022-04-08T00:55:15Z) - Description-based Label Attention Classifier for Explainable ICD-9
Classification [10.407041139832955]
We propose a description-based label attention classifier to improve the model explainability when dealing with noisy texts like clinical notes.
We evaluate our proposed method with different transformer-based encoders on the MIMIC-III-50 dataset.
arXiv Detail & Related papers (2021-09-24T15:31:38Z) - Hierarchical Representation based Query-Specific Prototypical Network
for Few-Shot Image Classification [5.861206243996454]
Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data.
Recent metric-based frameworks tend to represent a support class by a fixed prototype (e.g., the mean of the support category) and make classification according to the similarities between query instances and support prototypes.
We propose a Hierarchical Representation based Query-Specific Prototypical Network (QPN) to tackle the limitations by generating a region-level prototype for each query sample.
arXiv Detail & Related papers (2021-03-21T12:50:05Z) - A Meta-embedding-based Ensemble Approach for ICD Coding Prediction [64.42386426730695]
International Classification of Diseases (ICD) are the de facto codes used globally for clinical coding.
These codes enable healthcare providers to claim reimbursement and facilitate efficient storage and retrieval of diagnostic information.
Our proposed approach enhances the performance of neural models by effectively training word vectors using routine medical data as well as external knowledge from scientific articles.
arXiv Detail & Related papers (2021-02-26T17:49:58Z) - Analysis and tuning of hierarchical topic models based on Renyi entropy
approach [5.487882744996213]
tuning of parameters of hierarchical models, including the number of topics on each hierarchical level, remains a challenging task.
In this paper, we propose a Renyi entropy-based approach for a partial solution to the above problem.
arXiv Detail & Related papers (2021-01-19T12:54:47Z) - Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
We investigate three schemes to improve the model generalization ability for few-shot settings.
We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data.
We create new state-of-the-art results on both few-shot and training-free settings.
arXiv Detail & Related papers (2020-12-29T23:43:16Z) - Coherent Hierarchical Multi-Label Classification Networks [56.41950277906307]
C-HMCNN(h) is a novel approach for HMC problems, which exploits hierarchy information in order to produce predictions coherent with the constraint and improve performance.
We conduct an extensive experimental analysis showing the superior performance of C-HMCNN(h) when compared to state-of-the-art models.
arXiv Detail & Related papers (2020-10-20T09:37:02Z)
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.