Back to Patterns: Efficient Japanese Morphological Analysis with
  Feature-Sequence Trie
Naoki Yoshinaga
Accurate neural models are much less efficient than non-neural models and are
useless for processing billions of social media posts or handling user queries
in real time with a limited budget. This study revisits the fastest
pattern-based NLP methods to make them as accurate as possible, thus yielding a
strikingly simple yet surprisingly accurate morphological analyzer for
Japanese. The proposed method induces reliable patterns from a morphological
dictionary and annotated data. Experimental results on two standard datasets
confirm that the method exhibits comparable accuracy to learning-based
baselines, while boasting a remarkable throughput of over 1,000,000 sentences
per second on a single modern CPU. The source code is available at
https://www.tkl.iis.u-tokyo.ac.jp/~ynaga/jagger/