Using modern Japanese NLP tools for language learning

Japanese can be a formidable language for learners. With no spaces between words, three different writing systems, and a grammar that builds meaning by “gluing” components onto words, simply figuring out where one word ends and the next begins is a major hurdle.

As a coder, I am always looking for opportunities to leverage programmatic tools to optimize my daily tasks. Fortunately, the same Natural Language Processing (NLP) libraries that power translation apps and search engines can be a language learner’s best helper. Using appropriately, these NLP tools become an X-Ray machine for sentences, revealing their underlying structure.

In this post, I will review major Japanese NLP libraries in Python, comparing each library’s strengths and weaknesses while focusing on their utility for foreign learners. I will also show how they, alongside modern general-purpose Large Language Models (LLMs), can supercharge your learning.

Let’s go!

What are core NLP tasks?

Before diving into the libraries, let’s review the key tasks. In Japanese, many of these are performed simultaneously by a single tool.

• Tokenization (単語分割 - tango bunkatsu): The first and most critical step. This is the process of splitting a continuous string of text like 東京都に行く into meaningful words or “tokens”: ['東京都', 'に', '行く'] using a dictionary . Japanese tokenizers need to handle the language’s unique characteristics, such as the absence of spaces between words.

• Morphological Analysis (形態素解析 - keitaiso kaiseki): It breaks text into its smallest grammatical units, called morphemes. For each morpheme, it identifies its grammatical properties, including part-of-speech (POS) tagging and lemmatization.

  • Part-of-Speech (POS) Tagging (品詞タグ付け - hinshi tagu-zuke): Identifying the grammatical category of each token (e.g., noun, verb, particle, adjective).
  • Lemmatization (見出し語化 - midashigo-ka): Finding the “dictionary form” or base form of a word. For example, the lemma of the verb 食べた (tabeta, ate) is 食べる (taberu, to eat).

• Named Entity Recognition (NER) (固有表現抽出 - koyū hyōgen chūshutsu): Identifying and categorizing named entities like people, organizations, locations, and dates. For example, an NER scan for 東京都に行く will extract 東京都.

• Dependency Parsing (係り受け解析 - kakariuke kaiseki): Analyzing the grammatical structure of a sentence by identifying the syntactic relationships between words. A parser identifies the sentence’s subject, object, and verb, showing which words modify or depend on which other words. This reveals the sentence’s “who did what to whom” structure. Combined with a visualizer, we can view the sentence as a directed graph, where each word is a node, and the direct edges represent the dependencies between words. In a tree format, the root of the tree represents the main verb or clause.

• Phrase structure analysis: Identifying the phrases within a sentence and their relationships. It represents the sentence as a tree structure, where each node represents a phrase.

  While dependency parsing focuses on the relationships between individual words, phrase structure analysis focuses on the hierarchical structure of phrases. However, you can often infer phrase structures and their relationships from analyzing dependency parse trees. For example, you can group words that are connected by certain dependency relations (e.g., noun phrases, verb phrases).

By breaking down a sentence into phrases, you can quickly grasp the building blocks and general patterns of the language. Then, you can go even further by decomposing phrases into finer tokens, revealing additional grammatical information such as part-of-speech and lemma that helps you understand the nature of each word.

NLP libraries as grammar scanner

Let’s take a look at the most prominent libraries (with focus on Python support) for the above tasks!

MeCab

MeCab has long been a major player in Japanese tokenizer and morphological analysis. It’s incredibly fast, lightweight, and supports various dictionaries. It works by matching text against a large dictionary. MeCab can perform morphological analysis, with POS tagging and lemmatization.

• Strengths:
  • Speed: Blazingly fast, making it ideal for processing large amounts of text or for use in applications requiring real-time responses (like browser pop-up dictionaries).
  • Customization: You can use different dictionaries like IPA, Juman, or UniDic, each offering different tokenization philosophies and levels of detail. UniDic is particularly detailed, often breaking words into their smallest possible units.
• Weaknesses:
  • Stale: Not updated since 2013.
  • Install issues: Users report installation issues in some platforms.
  • Dictionary-dependent: It can struggle with neologisms (new words), slang, or names not present in its dictionary. Tokenization and analysis quality can vary depending on the dictionary used.
  • Accuracy: While good, its accuracy can sometimes be lower than newer, neural-network-based models on complex or ambiguous sentences.

Juman++

Juman++ is a modern Japanese tokenizer and morphological analyzer that uses a neural network-based approach. It’s designed to handle out-of-vocabulary words and has been shown to outperform MeCab in some cases.

It can also be used for dependency parsing and phrase structure analysis to identify sentence structures and phrases.

• Strengths: High accuracy , especially for out-of-vocabulary words, and doesn’t require dictionary maintenance.
• Weaknesses: Computationally expensive.

Sudachi

Sudachi includes both a tokenizer and morphological analyzer. It has a Python wrapper SudachiPy .

• Strengths:
  • Multiple Split Modes: This is its most distinguished feature. Sudachi can tokenize in three modes: Mode A (UniDic short unit words), Mode B (middle), and Mode C (long, typically compound words). This lets you see how smaller words combine to form larger concepts. Beginners can use Mode A to see every tiny component, while intermediate learners can use Mode C to grasp compound nouns, named entities and concepts. It provides a flexible lens for viewing sentence structure.
  • Different dictionaries: Sudachi dictionary stems from Unidic by adding neologisms and fixing inconsistencies between words. It offers 3 levels of dictionaries to install .

The following sample shows how to adjust the tokenizer’s split granularity

from sudachipy import Dictionary, SplitMode 

tokenizer = Dictionary().create()
sentence = "旅客機は羽田空港に引き返し、午後7時過ぎに着陸"
tokens = tokenizer.tokenize(sentence, SplitMode.A)   
print("tokens in SplitMode A ", [m.surface() for m in tokens])
tokens = tokenizer.tokenize(sentence, SplitMode.C)   
print("tokens in SplitMode C ", [m.surface() for m in tokens])

output:

tokens in SplitMode A  ['旅客', '機', 'は', '羽田', '空港', 'に', '引き', '返し', '、', '午後', '7', '時', '過ぎ', 'に', '着陸']
tokens in SplitMode C  ['旅客機', 'は', '羽田空港', 'に', '引き返し', '、', '午後', '7', '時', '過ぎ', 'に', '着陸']

spaCy

spaCy pipeline. Image credit: official website

spaCy is an All-in-one NLP framework. It’s language pipelines provides tokenization, POS tagging, morphological analysis, named entity recognition (NER), and dependency parsing. Parsed docs can be passed to a visualizer for untangling sentence grammar.

The prebuilt language models come in two main flavors:

1. Statistical Models (ja_core_news_sm/md/lg): These are efficient models based on CNN architectures. These models are trained on a large corpus of news and web text data and provide a balance between accuracy and performance. They’re suitable for most NLP tasks that don’t require state-of-the-art accuracy, including dependency parsing or morphological analysis, text classification, or entity recognition.

   • ja_core_news_sm: A small model (~10MB) that’s fast and efficient.

   • ja_core_news_md: A medium-sized model (~50MB) that provides a balance between accuracy and performance.

   • ja_core_news_lg: A large model (~500MB) that provides high accuracy but is computationally expensive

     The major difference between these models is that the lg models include one word vector for most words in the training data, while the md model prunes the vectors table to only include entries for the 20,000 most common words, mapping less frequent words to the most similar vector in the reduced table. The sm models do not use pretrained vectors. With the md and lg models, you can use the vectors to compute similarities between words and sentences.

2. Transformer Models (ja_core_news_trf): This pipeline uses a newer (and larger) BERT-based transformer model . It delivers higher accuracy, especially for complex grammatical parsing, but requires more computational resources and is slower. This is suitable for tasks that require high accuracy with contextual understanding, such as language translation, question answering, and text generation.

• Strengths:

  • Complete Pipeline: The spaCy framework provides tokenization and POS tagging (via SudachiPy split mode A for Japanese, using sudachidict-core dictionary), dependency parsing, NER, lemmatizatioi in a single pipeline. Its modular approach allows you to choose the components you want in the pipeline based on the job to be processed, and customize each of them.
  • Ease of Use: Unlike MeCab or cabocha which can pose installation challenges, A simple pip install spacy and python -m spacy download ja_core_news_md is all you need. The spaCy API is also extremely well-documented and intuitive.
  • Noun Chunking: Calling doc.noun_chunks can automatically identify “noun chunks”: the main noun plus all the words describing it (e.g., 現代の技術 - modern technology, , 私たちの生活を). This helps you see the core building blocks of the sentence.
  • Superb Visualization: The spaCy ecosystem provides several built-in visualizers such as deplacy , displacy , and Explacy . When the pipeline parser outputs a dependency parse tree, these tools take it to produce nice plots that help learners untangle long, complex sentences. You can visually see which adjective modifies which noun and what the subject of the verb is, clarifying the roles of particles like が, を, and に. The displacy visualizer can also take NER output to produce color blocks highlighting each entity identified.

    hop to this demo to see how displacy renders dependency graphs, and see how it visualizes named entities. The demo also provides ready to copy code to implement the plots.

  • Quick prototyping: Using [spacy-streamlit](https://github.com/explosion/spacy-streamlit) to rapidly build a streamlit frontend to test ideas and visualize models.
  • rule-based matching: You can use rules to search for tokens not just on their face values (like in regular expression), but based on their linguistic attributes such as POS, dependency, lemma and morphology. You can see how it works in this interactive demo .

• Weaknesses:

  • Slower: As a deep learning model, it’s significantly slower than dictionary-based tools like MeCab, making it less suitable for high-volume, real-time processing.

You can experience spaCy’s features without installing anything by going to its pipeline demo , choosing a language model and playing with the default text or your own. At the same time, since the demo was built with [spacy-streamlit](https://github.com/explosion/spacy-streamlit), you can also experience the extension’s ability too!

GiNZA

GiNZA is a Japanese NLP library built on top of spaCy and provides pre-trained models (both CNN and transformer-based) for sentence structure analysis. GiNZA’s models are trained on a variety of corpora, including the Japanese Wikipedia and the Balanced Corpus of Contemporary Written Japanese.

GiNZA offers several key advantages compared to spaCy’s native pipelines:

• Native bunsetsu support: Native API support to analyze bunsetsu and identify their dependencies.
• Universal Dependencies Standard: GiNZA is designed from the ground up to follow the Universal Dependencies (UD) standard. This means its grammatical dependency labels (nsubj, obj, etc.) are consistent with a global framework used for over 100 languages, making it a powerful tool for serious linguistic study.

Cabocha

CaboCha is a Japanese dependency parser that segment the sentence into bunsetsu and parse their dependencies. There’s also a python wrapper available.

Strengths:

• High accuracy: CaboCha has been shown to achieve high accuracy in dependency parsing tasks.
• Handling complex sentences: CaboCha can handle complex sentence structures, including those with multiple clauses and phrases.
• Native bunsetsu support:: Like GiNZA, it works as a bunsetsu chunker and parser by segmenting sentences into bunsetsu and then determine which bunsetsu depends on which other one. The output tree makes it intuitive to understand.

Weaknesses:

• Installation is not straightforward
  • not supported in Windows 64-bit Python
  • depends on MeCab installation
  • a Python wrapper makes the installation a bit easier

KNP

Developed by Kyoto University, KNP is a Dependency and Case Structure Analyzer using Juman++ for tokenization, then perform additional analysis in 3 levels: morphome, base_phrases and bunsetsu.

下鴨神社の参道は暗かった。
  文節区切り:　 下鴨神社の|参道は|暗かった。
  基本句区切り: 下鴨|神社の|参道は|暗かった。
  形態素区切り: 下鴨|神社|の|参道|は|暗かった|。

2 Python wrappers are available: pyknp and rhoknp , the latter which supports document-level analysis. There’s also a web-based wrapper.

Janome

Janome  is an easy to use and versatile library that offers not just tokenization, but also morphological analysis . It can also handle text processing tasks such as lowercase/upper case conversion, specific token filters, regular expression operation, compound noun filters, removing or keeping specific POS words, count word frequencies (similar to sklearn’s CountVectorizer ), etc.

The following sample shows how we can use the plain tokenizer to break down a sentence, versus leveraging the more complex Analyzer to build a pipeline to tokenize by compound nouns, remove punctuations and particles, and lowercase English alphabets. The result will also show POS tags and pronunciations.

from janome.analyzer import Analyzer
from janome.tokenfilter import*
from janome.tokenizer import Tokenizer  

s ='米Appleは、9月9日の午前10時（現地時間、日本時間は9月10日午前2時）から、オンラインイベント「言葉にできない。」を行うと発表した。'

t = Tokenizer()
tok=list(t.tokenize(s,wakati=True))
print(tok)

token_filters = [CompoundNounFilter(), POSStopFilter(['記号','助詞']), LowerCaseFilter()]
a = Analyzer(token_filters=token_filters)
for token in a.analyze(s):  
   print(token)

output:

['米', 'Apple', 'は', '、', '9', '月', '9', '日', 'の', '午前', '10', '時', '（', '現地', '時間', '、', '日本', '時間', 'は', '9', '月', '10', '日', '午前', '2', '時', '）', 'から', '、', 'オンライン', 'イベント', '「', '言葉', 'に', 'でき', 'ない', '。', '」', 'を', '行う', 'と', '発表', 'し', 'た', '。']
米apple	名詞,複合,*,*,*,*,米apple,ベイ*,ベイ*
9月9日	名詞,複合,*,*,*,*,9月9日,*ツキ*ニチ,*ツキ*ニチ
午前10時	名詞,複合,*,*,*,*,午前10時,ゴゼン*ジ,ゴゼン*ジ
現地時間	名詞,複合,*,*,*,*,現地時間,ゲンチジカン,ゲンチジカン
日本時間	名詞,複合,*,*,*,*,日本時間,ニッポンジカン,ニッポンジカン
9月10日午前2時	名詞,複合,*,*,*,*,9月10日午前2時,*ツキ*ニチゴゼン*ジ,*ツキ*ニチゴゼン*ジ
オンラインイベント	名詞,複合,*,*,*,*,オンラインイベント,オンラインイベント,オンラインイベント
言葉	名詞,一般,*,*,*,*,言葉,コトバ,コトバ
でき	動詞,自立,*,*,一段,未然形,できる,デキ,デキ
ない	助動詞,*,*,*,特殊・ナイ,基本形,ない,ナイ,ナイ
行う	動詞,自立,*,*,五段・ワ行促音便,基本形,行う,オコナウ,オコナウ
発表	名詞,サ変接続,*,*,*,*,発表,ハッピョウ,ハッピョー
し	動詞,自立,*,*,サ変・スル,連用形,する,シ,シ
た	助動詞,*,*,*,特殊・タ,基本形,た,タ,タ

jptranstokenizer

This is a tokenization wrapper for Hugging Face pretrained Japanese models as well as most of the tools listed above.

Comparing the tools

Each library has its strengths and weaknesses, and the choice ultimately depends on your specific needs.

• Tokenization and morphological analysis: MeCab and Juman++ are suitable choices, depending on your specific needs and the level of customization required.
• NER and POS tagging: spaCy’s Japanese model is a convenient and accurate choice.
• Dependency parsing: CaboCha, KNP and spaCy with GiNZA are all suitable choices.
• Visualization: spaCy

LLMs as personal tutor

So, where do Large Language Models like Gemini or GPT-5 fit in? Can they replace these specialized tools?

We can think of the traditional NLP libraries as precision instruments and LLMs as a conversational tutor. Modern LLMs, such as transformer-based models, have achieved state-of-the-art results in various NLP tasks, including those mentioned above.

• Strengths:
  • Zero startup time: No installation and programming experience needed. You can simply ask it questions in plain language.
  • Natural Language Interface: They’re convenient to use, as they often come with pre-trained models and easy-to-use interfaces. You can ask, “Explain the grammar of this sentence,” “Why is the particle は used here instead of が?,” or “Give me three more examples of this verb conjugation.”
  • Nuance & Context: LLMs excel at understanding context, slang, and subtle nuances that traditional parsers miss. They can explain why something is said a certain way.
  • Flexibility: They can perform all the same tasks (tokenization, NER, etc.) plus translation, summarization, and rephrasing, all on demand.
  • Interaction: You can have the LLM quiz you and build an adaptive learning plan based on your performance.
• Weaknesses:
  • Non-Deterministic: The output isn’t always consistent or structured, making it unsuitable for building automated tools that require predictable, machine-readable output.
  • Explanability: They can be less interpretable than traditional NLP tools.
  • Potential for “Hallucination”: An LLM can sometimes provide a confident but incorrect explanation of a grammar point.
  • Slower/Costlier: For batch processing, API calls are slower and more expensive than running a local traditional library.

For foreign language learners, LLMs can be a powerful tool for the following tasks:

• Language translation: Translate text from Japanese to your native language, helping with comprehension.
• Language explanation: Answer questions and provide explanations for Japanese text, helping with comprehension and grammar.
• Writing critique: Review your written Japanese to spot any issues and provide further guidance for improvement.
• Listening and speaking practice: Using multimodal models enables a learner to practice speaking and listening on any phrases.

X-ray view for learning

As a Japanese language learner, analyzing sentence structures can help you better comprehend the language and improve your reading and writing skills.

In this section, I’ll show you how we can employ NLP libraries as an X-ray machine into Japanese text and understand its underlying structure using dependency parsing and phrase structure analysis.

We will be using spaCy with the GiNZA model to reveal the text’s grammatical “skeleton”, and visualize this network. This intuitive tree diagram let you instantly see the text connections without interpreting raw text output.

example code

The following sample uses spaCy/GiNZA to tokenize a sentence, showing its POS and dependency. To “zoom out” a bit, it also extracts bunsetsu spans. Finally, it visualizes the phrase-level structure with deplacy and graphiviz.

Prereq:

1. Install GinZA with pip install -U ginza ja_ginza for the standard, non-Transformer library.
2. Install spaCy with pip install -U spacy. Here since we are using the language model from GinZA, we do not need to install spaCy’s Japanese model separately. If, however, we want to use spaCy’s model instead, do an additional install with python -m spacy download ja_core_news_md (for the medium size model).
3. Install deplacy and graphviz with pip install deplacy graphviz.

import spacy
import ginza
from spacy import displacy

# Load the GiNZA Japanese model
nlp = spacy.load("ja_ginza")

# Process a Japanese sentence
doc = nlp("太郎は花子が読んでいる本を次郎に渡した")

# Print tokens with POS and dependency head
for token in doc:
    print(f"{token.text:6} | {token.pos_:6} : {token.dep_} <-- {token.head.text}")

# Extract bunsetu spans
from ginza import bunsetu_spans
print("\nBunsetu phrases:")
for span in bunsetu_spans(doc):
    print(f"• {span.text}")

# Produce dependency tree with deplacy   
import deplacy
import graphviz
graphviz.Source(deplacy.dot(doc))

The result is

太郎     | PROPN  : nsubj <-- 渡し
は      | ADP    : case <-- 太郎
花子     | PROPN  : nsubj <-- 読ん
が      | ADP    : case <-- 花子
読ん     | VERB   : acl <-- 本
で      | SCONJ  : mark <-- 読ん
いる     | VERB   : fixed <-- で
本      | NOUN   : obj <-- 渡し
を      | ADP    : case <-- 本
次郎     | PROPN  : obl <-- 渡し
に      | ADP    : case <-- 次郎
渡し     | VERB   : ROOT <-- 渡し
た      | AUX    : aux <-- 渡し

Bunsetu phrases:
• 太郎は
• 花子が
• 読んでいる
• 本を
• 次郎に
• 渡した

Conclusion: A complementary toolkit

NLP libraries and LLMs excel in complementary areas:

• Use traditional parsers (like spaCy/GiNZA) as “Grammar Scanner.” They provide the fast, accurate, and structured what of a sentence. They are perfect for on-the-fly reading assistance and for building a solid mental model of the language’s atomic parts.
• Use an LLM as “Personal Tutor.” Turn to it for the why. When the parser’s output leaves you with questions about nuance, usage, or context, the LLM is your go-to resource for an interactive, conversational explanation.

By combining the structural precision of libraries like spaCy/GiNZA with the contextual knowledge and interaction of an LLM, a modern Japanese learner has an unprecedentedly powerful toolkit for deconstructing and mastering the language. Happy learning! 頑張ってください！