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Accurate neural models are much less efficient than non-neural models and areuseless for processing billions of social media posts or handling user queriesin real time with a limited budget. This study revisits the fastestpattern-based NLP methods to make them as accurate as possible, thus yielding astrikingly simple yet surprisingly accurate morphological analyzer forJapanese. The proposed method induces reliable patterns from a morphologicaldictionary and annotated data. Experimental results on two standard datasetsconfirm that the method exhibits comparable accuracy to learning-basedbaselines, while boasting a remarkable throughput of over 1,000,000 sentencesper second on a single modern CPU. The source code is available athttps://www.tkl.iis.u-tokyo.ac.jp/~ynaga/jagger/

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