An extension of LIME Ribeiro et. al 2016 for text classification. The main pipeline trains words embeddings using fastText, add noises to embedding space of documents, and identifies important words by analyzing how the noise affects the output probabilities of any text classification model. The results are visualized in d3.js.
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