Мәтін классификаторлары
Related Papers
Interpretable Neural Predictions with Differentiable Binary Variables
AbstractThe success of neural networks comes hand in hand with a desire for more interpretability. We focus on text classifiers and make them more interpretable by having them provide a …...
Topics to Avoid: Demoting Latent Confounds in Text Classification
AbstractDespite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. …...
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection
AbstractGenerating explanations for neural networks has become crucial for their applications in real-world with respect to reliability and trustworthiness. In natural language processing, existing methods usually provide important features which …...
Integrated Directional Gradients: Feature Interaction Attribution for Neural NLP Models
AbstractIn this paper, we introduce Integrated Directional Gradients (IDG), a method for attributing importance scores to groups of features, indicating their relevance to the output of a neural network model …...
The Role of Context in Vaccine Stance Prediction for Twitter Users
Public expression of vaccine related sentiment on social media platforms can be used in infor- mation surveillance applications to gain insight into vaccine hesitancy and its spread. Effective identification of …...
Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction
AbstractThe most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An important observation for natural language processing is that …...