Finding Candidate Words for the Dictionary

Use this skill when asked to find new words to add to

candidate_words.json

for later addition to the dictionary.

Workflow Overview

1. Determine the search strategy based on user request and dictionary maturity
2. Check each word against eligibility criteria (MANDATORY duplicate checks)
3. Add qualifying words to

   candidate_words.json

   using the manage_candidates script
4. Report what was added

Duplicate Prevention (AUTOMATIC)

The

manage_candidates.py add

command now AUTOMATICALLY checks for duplicates.

Duplicate Definition

A word is a duplicate ONLY if BOTH the headword AND reading match exactly.

• Homophones (same reading, different headword) are NOT duplicates
  • Example: 線香 (せんこう) and 先行 (せんこう) are different words
  • Example: 橋/箸/端 (all はし) are different words
• Homographs (same headword, different reading) are NOT duplicates
  • Example: 行く (いく) and 行く (ゆく) are different readings
  • Example: 明日 (あした) and 明日 (あす) are different readings

The script will show informational notes about homophones and homographs, but these do not block addition.

When you run:

python3 build/manage_candidates.py add "食べる" "たべる" "to eat"

The script will:

1. Check

   entries_index.json

   for exact match (both headword AND reading)
2. Check

   candidate_words.json

   for exact match (both word AND reading)
3. REFUSE to add ONLY if an exact match is found
4. Display informational notes about homophones/homographs (not blocks)

Example of Automatic Rejection

$ python3 build/manage_candidates.py add "食べる" "たべる" "to eat"
ERROR: Duplicate detected!
  Exact match in dictionary: 00396_taberu (食べる / たべる)

This word already exists. NOT adding to candidates.

Example of Informational Note (NOT a rejection)

$ python3 build/manage_candidates.py add "線香" "せんこう" "incense stick"
OK: Added 線香 (せんこう) to candidates.
Note: Homophones exist: 先行 (03773_senkou), 専攻 (01587_senkou)

Pre-Check Command (Optional)

If you want to check a word before attempting to add it:

python3 build/manage_candidates.py check "漢字" "かんじ"

Batch Checking (Optional)

To check multiple words at once before adding:

python3 build/check_duplicate.py --batch "食べる:たべる" "飲む:のむ" "書く:かく"

Near-Duplicate Patterns (Editorial Consideration)

The automatic check catches exact matches (same headword AND reading). These patterns require editorial judgment to decide if they should be separate entries or considered the same word:

• Verb forms: する verbs may exist as standalone nouns (勉強 vs 勉強する) - often separate entries
• Kanji variants: 見る/観る, 聞く/聴く - may share an entry if meanings overlap significantly
• Okurigana variations: 行なう/行う, 現われる/現れる - usually the same word, different spellings
• Prefix/suffix forms: Check if 大～ or ～的 forms warrant separate entries
• Reading variations: Long vowels (おう vs おお), particles in readings - may need separate entries

Note: Homophones (same reading, different kanji like 線香/先行) are always separate words and should each have their own entry.

Eligibility Criteria (ALL must be met)

1-1. Not Already in the Dictionary

Check that the word is NOT already in

entries_index.json

(by reading AND headword).

1-2. Not Already a Candidate

Check that the word is NOT already in

candidate_words.json

(by reading AND word).

1-3. Not a Proper Noun

EXCLUDE the following categories:

• Place names (東京, ニューヨーク, 富士山, etc.)
• Personal names (田中, マイク, etc.)
• Company/brand names (トヨタ, ソニー, etc.)
• Other proper nouns (specific event names, specific work titles, etc.)

Note: Proper nouns may be added systematically in a future phase.

Selection Criteria (At least ONE must be met)

A word qualifies for addition if it meets at least one of these criteria:

2-1. Similar Frequency/Centrality to Existing Entries

The word should have a usage frequency or centrality to contemporary Japanese similar to words already in

entries_index.json

.

How to assess:

• Consider whether an intermediate learner would encounter this word regularly
• Compare to existing entries at similar frequency levels
• Note: All new entries are assigned to the general tier (basic and core tiers are complete)

2-2. Semantic Relation to Existing Entries

The word is a common synonym, antonym, or related word to an entry already in the dictionary.

Types of semantic relations:

• Synonyms: Words with similar meanings at different registers or contexts
• Antonyms: Opposite meanings
• Semantic groups: Words belonging to the same category as existing entries

2-3. Modern Widespread Terms

Words that have come into widespread, stable usage in contemporary Japanese. This includes vocabulary from technology, media, lifestyle, and other areas of modern life. Focus on terms with established, lasting usage rather than ephemeral trends.

2-4. Informal/Colloquial Terms Useful for Learners

Well-known informal or colloquial terms that:

• Do not typically appear on formal vocabulary lists
• Are commonly encountered in everyday Japanese
• Would help learners understand natural speech

Exclude: Highly ephemeral slang, vulgar terms, discriminatory language

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Search Strategies

The dictionary has ~9,000 entries as of late January 2026. This is a mature dictionary with strong coverage of basic and intermediate vocabulary. Use a variety of strategies to find remaining gaps while maintaining high quality standards.

Vocabulary Tier Status

The basic and core tiers are complete (as of January 2026).

• Basic: ~800 entries (target met)
• Core: ~2,000 entries (target met)
• General: ~6,200 entries (all new vocabulary goes here)

All new candidate words will be assigned to the general tier. Focus on finding useful vocabulary for advanced learners, specialized topics, and vocabulary that complements existing entries.

Quality Over Quantity

IMPORTANT: An earlier attempt to harvest candidate words by extracting vocabulary from entry notes produced thousands of spurious or inappropriate candidates. Learn from this:

• Avoid bulk extraction approaches that generate candidates without careful vetting
• Each candidate should be individually evaluated for appropriateness before adding
• Prefer verified vocabulary sources (frequency lists, textbook vocabulary, JLPT lists) over automated extraction
• When in doubt, don't add - it's better to have fewer high-quality candidates than many questionable ones

Strategy: Corpus-Driven Gap Analysis

Use corpus frequency data to find common words still missing.

Method:

1. Consider the top 10,000 words in corpora like BCCWJ (Balanced Corpus of Contemporary Written Japanese)
2. Compare against entries_index.json
3. Words in the top 10,000 by frequency that aren't in the dictionary are high-priority candidates

Why effective: Guarantees discovered words are genuinely common.

Strategy: Collocational Mining

Find words that commonly appear with existing entries but aren't in the dictionary.

Method:

1. Take existing entries, especially verbs and adjectives
2. Consider their most common collocates (words they frequently appear with)
3. Check if those collocates are in the dictionary

Why effective: Finds words learners need to use existing vocabulary naturally.

Strategy: Register/Formality Pairs

For existing entries, find their register variants (formal ↔ informal, written ↔ spoken).

Method:

1. Take informal words in the dictionary, find their formal equivalents
2. Take formal/written words, find their colloquial equivalents
3. Check keigo (honorific) variants of common verbs

Why effective: Learners need multiple registers; dictionaries often have gaps here.

Strategy: Semantic Domain Exploration

Explore semantic domains to find gaps, but choose domains creatively based on what seems underrepresented rather than following a fixed list.

Method:

1. Browse existing entries to identify which semantic domains have coverage
2. Notice which domains seem thin or missing
3. Systematically check for gaps in those domains
4. Be creative—consider domains that are practical for daily life, work, study, or cultural understanding

Why effective: Ensures balanced coverage across the vocabulary learners need.

Strategy: Productive Pattern Completion

Systematically complete morphological patterns already partially in the dictionary.

Method:

1. Identify word-formation patterns that are productive in Japanese (compound verbs, adjectival derivations, noun compounds, etc.)
2. Check which common words using those patterns are missing
3. Add gaps

Why effective: These patterns are productive and predictable; gaps are easy to identify systematically.

Strategy: Cross-Reference Expansion

Expand from existing entries by finding mentioned but unlisted words.

Method:

1. Read recent dictionary entries
2. For each entry, identify synonyms, antonyms, and related words mentioned in notes or cross-references
3. Check if those related words are in the dictionary
4. Add missing ones as candidates

Why effective: Useful for incremental expansion from established content.

Strategy: Written vs Spoken Japanese Balance

Ensure the dictionary covers both written and spoken Japanese.

Method:

1. Check for gaps in formal/written vocabulary (news, academic, business contexts)
2. Check for gaps in casual/spoken vocabulary (conversation, sentence-final expressions, contracted forms)

Why effective: Dictionaries often skew toward one medium; this ensures balanced coverage.

Strategy: External Vocabulary List Cross-Reference

Compare against established vocabulary lists to find gaps.

Method:

1. Reference JLPT vocabulary lists (especially N2 and N1 levels for advanced vocabulary)
2. Check textbook vocabulary from major Japanese language textbook series
3. Compare against published frequency dictionaries
4. Look at vocabulary from Japanese language proficiency resources

Why effective: These lists represent expert-curated vocabulary that learners demonstrably need; gaps here are high-priority.

Strategy: Practical Situation Vocabulary

Think about vocabulary needed for specific real-world situations.

Method:

1. Consider common situations: shopping, dining, travel, medical visits, banking, workplace, etc.
2. Think about what vocabulary a learner would need in each situation
3. Check if essential words for these situations are in the dictionary

Why effective: Ensures the dictionary is practically useful, not just academically complete.

Strategy: Media and Cultural Vocabulary

Identify vocabulary commonly encountered in Japanese media and culture.

Method:

1. Consider words frequently used in news, TV, films, manga, and online content
2. Look for cultural terms that learners need to understand Japanese society
3. Include vocabulary for discussing current events and contemporary life

Why effective: Helps learners engage with authentic Japanese content.

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Creative Exploration

Don't limit yourself to the strategies above. Each search session should involve creative thinking about what vocabulary learners need that might be missing. Consider:

• What practical situations would learners encounter?
• What vocabulary would help them understand media, conversations, or texts?
• What semantic fields feel underrepresented?
• What word types (verbs, adjectives, adverbs, etc.) might have gaps?

The goal is comprehensive, balanced coverage—not deep exploration of any single category at the expense of others.

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Adding Candidates

After identifying qualifying words, add them using:

python3 build/manage_candidates.py add "漢字表記" "ひらがな読み" "brief English note"

IMPORTANT: Readings must be in hiragana, not katakana.

Even for loanwords with katakana headwords, the reading must be hiragana:

# Correct:
python3 build/manage_candidates.py add "スキー" "すきー" "skiing"

# Wrong (katakana reading):
python3 build/manage_candidates.py add "スキー" "スキー" "skiing"

Notes field guidance:

• Brief English gloss or description
• Can include part of speech hint
• Keep under 50 characters

Example:

python3 build/manage_candidates.py add "提案" "ていあん" "proposal, suggestion"

Output Format

After adding candidates, report:

1. Number of words added
2. Summary of categories/sources (aim for variety)
3. Any notable gaps identified for future sessions

Quality Reminders

• Quality over quantity: Each candidate should be individually vetted; avoid bulk extraction approaches
• Duplicates are blocked automatically: The

  manage_candidates.py add

  command will refuse to add duplicates
• Watch for near-duplicates: The automatic check catches exact matches; manually verify for verb forms, kanji variants, etc.
• Breadth over depth: Aim for broad coverage across many domains rather than deep coverage of a few
• General tier only: All new entries are assigned to the general tier (basic and core are complete)
• Learner utility: Prioritize words an intermediate-to-advanced learner would benefit from knowing
• No proper nouns: Save those for systematic addition later
• Stable vocabulary: Avoid ephemeral slang or highly specialized jargon
• No archaic or dialect terms: Exclude vocabulary that most modern Japanese speakers would not know or use
• Creative variety: Each session should explore different areas; avoid repeatedly searching the same categories
• Use batch checks: When planning which words to add:

  python3 build/check_duplicate.py --batch "word1:reading1" ...

• When in doubt, skip: It's better to pass on a questionable word than to add inappropriate candidates