.png){kind=icon}
Return
Improving text classification via computing category correlation matrix from text graph
.png){kind=icon}
PRE
AI
DOI：10.1016/j.csl.2024.101688.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In text classification task, models have shown remarkable accuracy across various datasets. However, confusion often arises when certain categories within the dataset are too similar, causing misclassification of certain samples. This paper proposes an improved method for this problem, through the creation of a three-layer text graph for the corpus, which is used to calculate the Category Correlation Matrix (CCM). Additionally, this paper introduces categoryadaptive contrastive learning for text embedding from the encoder, enhancing the model's ability to distinguish between samples in confusable categories that are easily confused. Soft labels are generated using this matrix to guide the classifier, preventing the model from becoming overconfident with one-hot vectors. The efficacy of this approach was demonstrated through experimental evaluations on three text encoders and six different datasets.
Keywords:
Natural language processing
Text classification
Contrastive learning
Soft label learning
Journal
C
IF:
3.4
Papers:
1.5K
Citations:
2.6K
Organization
Cited Papers
ShareSaveShareSaveMachine learning for email spam filtering: review, approaches and open research problems.png)
HELIYON
OAAI
HELIYONIF3.6
OAAIShareSaveShareSaveThe Potential of Smart Factories and Innovative Industry 4.0 Technologies—A Case Study of Different-Sized Companies in the Furniture Industry in Central EuropeForests
OAAI
ForestsIF0
OAAIShareSaveShareSave
View more