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Joint Embedding of Words and Labels for Text Classification

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Figure 1: Illustration of different schemes for doc-ument representationshas been devoted to directly aggregating word em-beddingembeddingdenotes the cosine similarity betweenclidean space), and leveraging the “compatibility”Gthe attention score z
Table 2: Summary statistics of five datasets, in-cluding the number of classes, number of trainingsamples and number of testing samples.
Table 3: Test Accuracy on document classification tasks, in percentage. ⋄ We ran Bi-BloSAN using theauthors’ implementation; all other results are directly cited from the respective papers.
Figure 2: Comprehensive study of LEAM, including convergence speed, performance vs proportion oflabeled data, and impact of hyper-parameter
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