A self-adaptive classifier for efficient text-stream processing is proposed. The proposed classifier adaptively speeds up its classification while processing a given text stream for various NLP tasks. The key idea behind the classifier is to reuse results for past classification problems to solve forthcoming classification problems. A set of classification problems commonly seen in a text stream is stored to reuse the classification results, while the set size is controlled by removing the least-frequently-used or least-recently-used classification problems. Experimental results with Twitter streams confirmed that the proposed classifier applied to a state-of-the-art base-phrase chunker and dependency parser speeds up its classification by factors of 3.2 and 5.7, respectively.
['Naoki Yoshinaga', 'Masaru Kitsuregawa']
A Self-adaptive Classifier for Efficient Text-stream Processing