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Joint Word-Label Embedding for Text Classification by moavoting943 is a document available to read on EtoBox.

The document presents a novel approach to text classification called the Label-Embedding Attentive Model (LEAM), which integrates word and label embeddings into a joint space to enhance classification performance. By employing an attention mechanism that focuses on the compatibility between text sequences and labels, the model achieves state-of-the-art results on various datasets while maintaining interpretability and computational efficiency. The authors demonstrate the effectiveness of LEAM through extens

Author

moavoting943

Language

EN