Antigravity Agent
161 lines
4.8 KiB
Markdown
161 lines
4.8 KiB
Markdown
---
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id: wiki-2026-0508-term-frequency-inverse-document-
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title: Term Frequency-Inverse Document Frequency
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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: [TF-IDF, tfidf, classic IR baseline]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.95
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verification_status: applied
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tags: [ir, nlp, retrieval, baseline, sklearn]
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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: scikit-learn
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---
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# Term Frequency-Inverse Document Frequency
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## 매 한 줄
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> **"매 term frequency × inverse document frequency — 매 word's importance 의 corpus context 매 weight"**. Karen Spärck Jones (1972) 의 IDF formalization. 2026 매 dense retrieval (BGE, E5) 매 default 매도 매 baseline + hybrid (BM25 + dense) 의 second stage 매 still ubiquitous.
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## 매 핵심
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### 매 Formula
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- **TF**: term의 doc 매 count (raw / log-normalized / frequency).
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- **IDF**: `log(N / df_t)` — 매 N corpus size, df_t = doc 매 t의 contains 의 count.
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- **TF-IDF**: TF(t,d) × IDF(t).
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- **L2 norm**: 매 cosine 의 prepare.
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### 매 Variants
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- Raw TF / log(1+TF) / sublinear.
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- IDF smoothing: `log((1+N)/(1+df)) + 1`.
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- BM25: 매 TF saturation + length normalization 의 add.
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### 매 응용
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1. Search baseline (sklearn + scikit-learn).
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2. Hybrid retrieval — 매 BM25 + dense embedding의 reciprocal-rank fuse.
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3. Feature extraction 매 classical ML (logistic regression, SVM).
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4. Keyword extraction (top-k tfidf).
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## 💻 패턴
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### sklearn TF-IDF
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```python
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from sklearn.feature_extraction.text import TfidfVectorizer
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corpus = [
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"the cat sat on the mat",
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"the dog ate the bone",
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"cats and dogs are pets",
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]
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vec = TfidfVectorizer(stop_words="english", sublinear_tf=True, ngram_range=(1, 2))
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X = vec.fit_transform(corpus) # sparse (n_docs, n_features)
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print(vec.get_feature_names_out())
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```
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### Cosine search
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```python
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from sklearn.metrics.pairwise import cosine_similarity
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q = vec.transform(["pet animals"])
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sims = cosine_similarity(q, X).flatten()
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ranking = sims.argsort()[::-1]
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```
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### Manual IDF (educational)
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```python
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import math
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from collections import Counter
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def compute_idf(corpus_tokens):
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N = len(corpus_tokens)
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df = Counter()
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for tokens in corpus_tokens:
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for t in set(tokens):
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df[t] += 1
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return {t: math.log((N + 1) / (df_t + 1)) + 1 for t, df_t in df.items()}
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```
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### BM25 (preferred over plain TF-IDF for IR)
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```python
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from rank_bm25 import BM25Okapi
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tokenized = [doc.lower().split() for doc in corpus]
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bm25 = BM25Okapi(tokenized, k1=1.5, b=0.75)
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scores = bm25.get_scores("pet animals".split())
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```
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### Hybrid search (2026 standard)
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```python
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import numpy as np
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def rrf(rankings, k=60):
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scores = {}
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for ranking in rankings:
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for rank, doc_id in enumerate(ranking):
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scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank)
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return sorted(scores, key=scores.get, reverse=True)
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bm25_top = bm25.get_top_n("query".split(), corpus, n=100)
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dense_top = dense_index.search("query", k=100)
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final = rrf([bm25_top, dense_top])[:10]
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```
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### Top keyword extraction
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```python
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def top_keywords(doc_idx, vec, X, k=10):
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row = X[doc_idx].toarray().flatten()
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feats = vec.get_feature_names_out()
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top = np.argsort(-row)[:k]
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return [(feats[i], row[i]) for i in top]
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```
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### Persistence
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```python
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import joblib
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joblib.dump((vec, X), "tfidf_index.joblib")
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vec, X = joblib.load("tfidf_index.joblib")
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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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| 매 small corpus + interpretability | TF-IDF (sklearn) |
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| 매 medium corpus + better recall | BM25 |
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| 매 semantic / paraphrase | Dense (BGE-M3, E5) |
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| 매 production search | Hybrid (BM25 + dense + RRF) |
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| 매 keyword extraction / explanation | Plain TF-IDF top-k |
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**기본값**: 매 BM25 baseline → 매 hybrid + reranker (cross-encoder) for 2026 production.
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## 🔗 Graph
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- 부모: [[Information Retrieval]] · [[Bag of Words]]
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- 변형: [[BM25]] · [[BM25F]]
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- 응용: [[Search Engine]] · [[Hybrid Retrieval]] · [[RAG]]
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- Adjacent: [[Cosine Similarity]] · [[Dense Retrieval]] · [[Cross-Encoder Rerank]]
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## 🤖 LLM 활용
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**언제**: 매 small corpus 매 lookup, 매 RAG 의 sparse channel, 매 explainability ("matched on 'mat', 'cat'").
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**언제 X**: 매 paraphrase / multilingual 매 weak — 매 dense 의 prefer.
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## ❌ 안티패턴
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- **TF-IDF 만으로 production search**: 매 paraphrase miss.
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- **No stopword / lowercasing**: 매 noisy features.
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- **Same vectorizer not pickled**: 매 train/serve mismatch.
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- **No length normalization**: 매 long docs 의 unfair advantage (use BM25 또는 normalize).
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## 🧪 검증 / 중복
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- Verified (Spärck Jones 1972; Manning IR Book Ch.6; sklearn TfidfVectorizer 2026).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — TF-IDF formula + sklearn + BM25 + hybrid RRF |
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