https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network
Multi-Label Text Classification using Attention-based Graph Neural Network
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According to Wikipedia "In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to."
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Multi-Label Text Classification using Attention-based Graph Neural Network
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
Investigating Capsule Networks with Dynamic Routing for Text Classification
Investigating Capsule Networks with Dynamic Routing for Text Classification
ML-Net: multi-label classification of biomedical texts with deep neural networks
ML-Net: multi-label classification of biomedical texts with deep neural networks
DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.