search

Search the awesome-japanese-nlp-resources database for: "$ARGUMENTS"

Instructions

Step 0 — Validate input

If $ARGUMENTS is empty or blank, stop immediately and output:

Usage: /awesome-japanese-nlp-resources:search <query>

Examples:
  /awesome-japanese-nlp-resources:search morphological analysis
  /awesome-japanese-nlp-resources:search BERT
  /awesome-japanese-nlp-resources:search named entity recognition
  /awesome-japanese-nlp-resources:search text classification dataset
  /awesome-japanese-nlp-resources:search sentence embedding

Please pass the keyword(s) you want to search for as the argument.

---

使い方: /awesome-japanese-nlp-resources:search <query>

クエリ例:
  /awesome-japanese-nlp-resources:search 形態素解析
  /awesome-japanese-nlp-resources:search BERT
  /awesome-japanese-nlp-resources:search 固有表現認識
  /awesome-japanese-nlp-resources:search テキスト分類 データセット
  /awesome-japanese-nlp-resources:search 文埋め込み

検索したいキーワードを引数に指定してください。

Do not proceed to Step 1 if $ARGUMENTS is empty.

Step 1 — Interpret the query

The user's query is: "$ARGUMENTS"

The data descriptions are in English, so always convert the query intent to English keywords before searching.

Keyword rules — read before choosing keywords:

1. Use stems, not full words. Substring match is used, so morpholog catches "morphology", "morphological", "morphological analyzer". Other examples: embed → embedding/embeddings, classif → classification/classifier, translat → translation/translate, generat → generation/generative, segment → segmentation/segmenter, recogni → recognition/recognizer, extract → extraction/extractor, retriev → retrieval/retrieve.
2. Add domain-specific tool names. When the query maps to a known NLP domain, include the well-known tool names present in the database:

Domain (Japanese query hint)	Stem keywords	Tool names to add
形態素解析 / morphological analysis	morpholog, segment	mecab, janome, sudachi, kytea, kuromoji, jumanpp, nagisa
固有表現認識 / NER	named entit, NER, recogni	ginza, spacy, knp
係り受け解析 / dependency parsing	depend, parse, syntax	cabocha, knp, ginza, spacy
文章分類 / text classification	classif, sentiment, categor	bert, fasttext
感情分析 / sentiment analysis	sentiment, emotion, opinion	oseti, wrime
埋め込み / word vectors / embeddings	embed, vector, represent	word2vec, fasttext, bert, sbert
事前学習モデル / pretrained model	pretrain, language model, bert, gpt	bert, gpt, llama, rinna, elyza, calm, swallow
テキスト生成 / text generation	generat, language model	gpt, llm, llama, rinna, elyza
機械翻訳 / machine translation	translat, machine translation	opus, marian, fairseq
音声認識 / speech recognition	speech, recogni, audio, asr	whisper, julius, espnet
音声合成 / text-to-speech	speech, synthesis, tts	voicevox, espnet
質問応答 / QA	question, answer, qa	bert, t5
要約 / summarization	summari, abstract	bart, t5, pegasus
辞書・IME / dictionary	dict, lexicon, ime	mecab, sudachi, mozc
コーパス・データセット / corpus	corpus, dataset, annot	(rely on stems)
チュートリアル / learning	tutorial, introduc, learn	(rely on stems)
OCR / 光学文字認識	ocr, optical character, recogni	manga-ocr, donut, tesseract
RAG / 検索拡張生成	retriev, rag, embed	ruri, glucose, faiss
ファインチューニング / fine-tuning	fine-tun, finetun, lora, peft	lora, peft, qlora
ベンチマーク・評価 / benchmark	benchmark, evaluat, jglue	llm-jp-eval, jglue, nejumi