refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-text-mining
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title: Text Mining
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Text Analytics, Information Extraction]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [nlp, text-mining, information-extraction]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: spaCy / LLM-based
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---
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# Text Mining
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## 매 한 줄
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> **"매 unstructured text → structured signal"**. Text mining 매 large text corpora 에서 patterns / entities / relationships / sentiment 의 extract 하는 분야. 매 traditional (TF-IDF, NER models) 에서 매 LLM-based extraction (structured output, function calling) 으로 매 paradigm shift.
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## 매 핵심
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### 매 traditional pipeline
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- **Tokenization** → **POS tagging** → **NER** → **dependency parsing** → **sentiment** → **topic modeling**.
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- Tools: spaCy, NLTK, scikit-learn (TF-IDF + classifiers), gensim (LDA).
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- 매 production NER: spaCy `transformers` pipeline or fine-tuned BERT.
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### 매 modern (LLM-based)
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- **Structured output** — 매 LLM 이 JSON schema 의 fill (Claude tool use, OpenAI structured output, Outlines).
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- **Few-shot extraction** — 매 fine-tune 없이 매 5 examples 만으로 task 의 정의.
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- **Long-context** — 매 200k+ token document 의 single-shot processing.
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- 매 cost trade-off: spaCy NER ~$0.0001/doc vs LLM ~$0.01/doc — 매 batch + small model (Haiku, gpt-4o-mini) 으로 reduce.
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### 매 응용
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1. Resume parsing, contract analysis (entity + clause extraction).
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2. Customer feedback aggregation (sentiment + topic).
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3. Biomedical literature mining (gene/protein/disease NER).
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## 💻 패턴
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### spaCy NER (traditional)
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```python
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import spacy
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nlp = spacy.load("en_core_web_trf")
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doc = nlp("Apple acquired Anthropic for $50B in March 2025.")
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for ent in doc.ents:
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print(ent.text, ent.label_)
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# Apple ORG, Anthropic ORG, $50B MONEY, March 2025 DATE
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```
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### TF-IDF + classifier
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```python
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.pipeline import Pipeline
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pipe = Pipeline([
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("tfidf", TfidfVectorizer(ngram_range=(1, 2), max_features=20000)),
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("clf", LogisticRegression(max_iter=1000)),
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])
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pipe.fit(X_train, y_train)
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```
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### LLM structured extraction (Claude)
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```python
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from anthropic import Anthropic
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from pydantic import BaseModel
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class Contract(BaseModel):
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parties: list[str]
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effective_date: str
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total_value_usd: float | None
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governing_law: str | None
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client = Anthropic()
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=2000,
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tools=[{
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"name": "extract_contract",
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"input_schema": Contract.model_json_schema(),
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}],
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tool_choice={"type": "tool", "name": "extract_contract"},
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messages=[{"role": "user", "content": contract_text}],
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)
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data = Contract(**resp.content[0].input)
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```
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### Topic modeling (BERTopic)
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```python
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from bertopic import BERTopic
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from sentence_transformers import SentenceTransformer
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embed = SentenceTransformer("BAAI/bge-large-en-v1.5")
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topic_model = BERTopic(embedding_model=embed, min_topic_size=10)
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topics, probs = topic_model.fit_transform(docs)
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topic_model.get_topic_info()
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```
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### Long-context document QA
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```python
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# 200k token contract → single LLM call
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=4000,
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messages=[{"role": "user", "content": [
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{"type": "text", "text": contract_full_text,
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"cache_control": {"type": "ephemeral"}},
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{"type": "text", "text": "Extract all change-of-control provisions."},
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]}],
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)
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```
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### Hybrid (LLM + regex precheck)
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```python
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import re
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DATE_RE = re.compile(r"\b\d{4}-\d{2}-\d{2}\b")
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candidates = DATE_RE.findall(text)
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# 매 LLM 의 candidate 만 disambiguate — 매 cost reduce
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| High-volume (M docs/day) NER | spaCy / fine-tuned BERT |
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| Complex schema, low volume | LLM structured output |
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| Topic discovery | BERTopic / embeddings + clustering |
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| Sentiment | Fine-tuned RoBERTa or LLM |
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| Long documents (>50k tokens) | LLM with caching |
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| Domain-specific (legal, medical) | Fine-tune + LLM hybrid |
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**기본값**: 매 prototype LLM, 매 production 은 LLM (low volume) or distilled fine-tuned model (high volume).
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## 🔗 Graph
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- 부모: [[Information Retrieval]]
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- 변형: [[Named-Entity-Recognition]] · [[Sentiment-Analysis]]
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- 응용: [[RAG]] · [[Search]]
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- Adjacent: [[Embeddings]]
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## 🤖 LLM 활용
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**언제**: 매 unstructured text corpus 의 query / extract / classify, schema-driven extraction, low-to-medium volume.
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**언제 X**: 매 milli-second latency 의 필요 (real-time chat moderation) — 매 small distilled model.
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## ❌ 안티패턴
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- **Regex-only complex extraction**: 매 brittle — 매 LLM hybrid 로 graceful.
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- **No evaluation set**: 매 LLM 매 hallucinate — 매 ground-truth eval 의 maintain.
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- **Full-document LLM 의 every query**: 매 cache or pre-extract structured DB.
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- **Unicode normalization 의 skip**: 매 Korean/CJK text 매 NFC normalize 필수.
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## 🧪 검증 / 중복
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- Verified (spaCy 3.x docs, Anthropic structured output guide, BERTopic, 2024-2026 NLP practice).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — traditional + LLM-based extraction patterns |
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