Back to Patterns: Efficient Japanese Morphological Analysis with Feature-Sequence Trie
Naoki Yoshinaga · 2023
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 patternbased 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.