Fast Nearest-neighbor Classification Using RNN In Domains With Large Number Of Classes

Abstract

In scenarios involving text classification where the number of classes is large (in multiples of 10000s) and training samples for each class are few and often verbose, nearest neighbor methods are effective but very slow in computing a similarity score with training samples of every class. On the other hand, machine learning models are fast at runtime but training them adequately is not feasible using few available training samples per class. In this paper, we propose a hybrid approach that cascades 1) a fast but less-accurate recurrent neural network (RNN) model and 2) a slow but more-accurate nearest-neighbor model using bag of syntactic features. Using the cascaded approach, our experiments, performed on data set from IT support services where customer complaint text needs to be classified to return top-\(N\) possible error codes, show that the query-time of the slow system is reduced to \(1/6^\{th\}\) while its accuracy is being improved. Our approach outperforms an LSH-based bas

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• arxiv keysingh2017fast

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