Long Document Classification using Hierarchical Attention Networks
Keywords:
long document classification, word2vec, sentence2vec, attention modelAbstract
Online comments and reviews are the primal exponents of the current era, which needs thorough conceptual discriminations and opinion analysis. Full-text analysis using document classification methods are traditional approach. Deep learning methods are employed on long legal documents for text classification. The word level classification semantically signifies the sense of classification. Statistical methods exist like TF, TF/PDF, and TF/IDF. Document classification is sentence-level classification using sentence vectors mentioned in hitherto successful research consensus. The challenge lies in long legal documents classifying based on the sense in sentences. The article represents challenges in the existing propositions and ideas for implementing long document classification using attention learning. A CNN-based attention learning model is described for classification on BBC Web News datasets. The results are appraised using performance evaluation metrics and RoC graph and have accomplished estimated accuracy of 96%.
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