# Back to Patterns: Efficient Japanese Morphological Analysis with Feature-Sequence Trie

arXiv: https://arxiv.org/abs/2305.19045 · HTML: https://mlsanity.com/paper/2305.19045/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 2 of 2)

- [Efficient Methods for Natural Language Processing: A Survey](https://mlsanity.com/paper/2209.00099/) (2022)
- [Universal Word Segmentation: Implementation and Interpretation](https://mlsanity.com/paper/1807.02974/) (2018)

## Built on by (most cited first, 1 of 1)

- [Recent advancements in computational morphology : A comprehensive survey](https://mlsanity.com/paper/2406.05424/) (2024)

## Abstract (arXiv)

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/