Information Extraction Advanced

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Information extraction is converting unstructured documents (emails, PDFs, contracts, invoices) into structured data. Advanced techniques: regex patterns, named entity recognition (NER), relation extraction, semantic parsing. Mastery takes 6-8 weeks of NLP + regex + labeling data. Senior extractors earn 25-40% premium because extraction unlocks $1M+ in automation (accounting, legal, logistics). It's rare: requires NLP literacy + domain expertise + judgment (when is 95% accuracy 'good enough'?).

Information Extraction Advanced이란

Information Extraction (IE) is converting unstructured documents (contracts, invoices, emails, PDFs) into structured, searchable data. Advanced IE uses NLP techniques: named entity recognition (NER) to find people, companies, dates; relation extraction to find "who hired whom"; semantic parsing to understand "Company X's revenue was $1M". You move from unstructured ("John Smith joined Acme Corp on Jan 15") to structured (name: "John Smith", company: "Acme Corp", date: "2025-01-15", action: "joined").

🔧 도구 & 생태계

spaCy NLPNLTKTransformers (Hugging Face)RegexNamed Entity RecognitionRelation extractionPDF parsing toolsLLMs (GPT, Claude)

💰 지역별 연봉

지역	주니어	미들	시니어
USA	$88k	$145k	$230k
UK	$54k	$88k	$140k
EU	$60k	$98k	$155k
CANADA	$92k	$150k	$240k

🎓 자격증

spaCy NLP Course Stanford NLP Course Information Extraction (Chen & Ji)

❓ 자주 묻는 질문

What's the difference between information extraction and information retrieval?

Retrieval = finding relevant documents (given query). Extraction = pulling structured fields from documents. Example: IR finds 'contracts mentioning payment terms'; IE extracts 'payment_amount, due_date, currency' from those contracts.

When should I use regex vs ML for extraction?

Regex: 90% of cases. Format is consistent (invoices always have 'Total: $XXX' in same position). Use regex, it's fast, interpretable. ML: 10% of cases. Format varies wildly (contract terms scattered across 20 pages, different format per vendor). Use ML + labeled data.

How much labeled data do I need for training an NER model?

500-1000 labeled examples for decent performance (80% accuracy). 5000+ for production (95%+ accuracy). Weak supervision can reduce: use dictionaries, distant supervision. Start small, label incrementally.

What's hallucination in IE and how do I prevent it?

LLM hallucination: model invents data if confidence is low. Example: 'extract phone number' but PDF has no phone → LLM makes up a number. Prevent: 1) Use confidence thresholds (only extract if >95% sure). 2) Validate extracted data (is phone number valid format?). 3) Reject low-confidence extractions instead of guessing.

How do I handle multi-page documents?

Split document into pages or sections. Extract from each section independently. Merge results (combine extracted entities). Watch for: a field on page 1, reference on page 3 (need linking). Use cross-page context for complex cases.

What's the difference between NER and relation extraction?

NER identifies entities (PERSON, ORG, DATE). RE identifies relationships (ORG hired PERSON on DATE). NER is prerequisite for RE. Example: NER extracts 'John Smith' (PERSON) + 'Acme Corp' (ORG). RE extracts 'John Smith works_for Acme Corp'.