What is Information Extraction?
Information Extraction is an AI technique that automatically identifies and pulls structured data such as names, dates, monetary values, and relationships from unstructured text sources like documents, emails, and web pages, converting free-form content into organized, queryable information.
What Is Information Extraction?
Information Extraction (IE) is a branch of Natural Language Processing that focuses on automatically identifying specific pieces of structured information from unstructured or semi-structured text. Rather than trying to understand the full meaning of a document, IE targets particular data points — names of people, companies, dates, monetary amounts, locations, relationships between entities, and other facts — and organizes them into a structured format.
Think of it as an automated analyst who reads through thousands of documents and pulls out exactly the data points you need, organized neatly in a spreadsheet or database. What would take a team of humans days or weeks, an information extraction system can accomplish in minutes.
For business leaders, information extraction is one of the most immediately practical AI capabilities available. Every company sits on vast amounts of unstructured text — contracts, invoices, emails, reports, news articles — that contain valuable data locked inside prose paragraphs. IE unlocks that data and makes it actionable.
How Information Extraction Works
Information extraction typically involves several interconnected tasks:
Named Entity Recognition (NER)
The foundation of most IE systems, NER identifies and classifies mentions of specific entities in text. These typically include person names, organization names, locations, dates, monetary values, and percentages. For example, from the sentence "Pertama Partners signed a $2M deal with Tokopedia in Jakarta on March 15," NER would extract: Organization (Pertama Partners, Tokopedia), Amount ($2M), Location (Jakarta), Date (March 15).
Relation Extraction
Beyond identifying individual entities, relation extraction determines how entities are connected. From the same sentence, it would identify that Pertama Partners and Tokopedia have a "deal" relationship, and that the deal is associated with Jakarta as a location and $2M as a value.
Event Extraction
This task identifies specific events described in text, along with their participants, timing, and other attributes. For instance, extracting "acquisition" events from news articles, including who acquired whom, for how much, and when.
Template Filling
The extracted information is organized into predefined templates or schemas. For a contract, this might mean filling fields like parties involved, effective date, termination date, payment terms, and key obligations.
Business Applications of Information Extraction
Contract and Legal Document Analysis IE systems can scan contracts to extract key terms, obligations, deadlines, and financial commitments. Law firms and corporate legal teams in Southeast Asia use this to review contracts faster, identify risks, and ensure compliance across different jurisdictions with varying legal frameworks.
Financial Document Processing Banks and financial institutions use IE to extract data from loan applications, financial statements, invoices, and regulatory filings. This accelerates processing times and reduces manual data entry errors.
Supply Chain Documentation Manufacturing and logistics companies process enormous volumes of purchase orders, shipping documents, and customs declarations. IE automates the extraction of product names, quantities, prices, origin and destination information, and compliance-related data.
News and Market Intelligence IE systems can monitor news feeds, press releases, and industry publications to automatically extract information about competitor activities, market developments, funding rounds, leadership changes, and regulatory updates.
Resume and Talent Screening HR departments use IE to extract skills, experience, education, and certifications from resumes, enabling faster candidate screening and more effective talent matching.
Information Extraction in Southeast Asian Markets
The Southeast Asian business environment creates specific use cases for IE:
- Cross-border trade documentation: Companies operating across ASEAN deal with trade documents in multiple languages and formats. IE can standardize data extraction across documents written in Bahasa Indonesia, Thai, Vietnamese, and English
- Regulatory compliance: Different ASEAN countries have varying regulatory requirements. IE helps companies extract and track compliance-relevant information from documents across multiple jurisdictions
- Multi-format challenges: Business documents in the region come in diverse formats including PDFs, scanned images, handwritten forms, and digital text. Modern IE systems combining OCR with NLP can handle this variety
- Banking and fintech: Southeast Asia's rapidly growing fintech sector relies on IE for Know Your Customer (KYC) document processing, transaction monitoring, and regulatory reporting
Accuracy and Challenges
Information extraction is not perfect, and business leaders should understand its limitations:
- Ambiguity: The same word can mean different things in different contexts. "Apple" might be a company or a fruit. Good IE systems use context to resolve such ambiguity
- Incomplete information: Documents may contain implied information that is obvious to human readers but difficult for machines to extract
- Domain specificity: An IE system trained on news articles may perform poorly on legal contracts. Domain-specific training is often necessary
- Language quality: Poorly written text, OCR errors from scanned documents, and informal language all reduce extraction accuracy
Getting Started with Information Extraction
- Audit your document workflows — Identify where employees spend the most time manually extracting data from documents
- Define your extraction targets — Specify exactly which data points you need extracted (names, dates, amounts, relationships)
- Evaluate tools — Cloud-based IE services from AWS, Google, and Azure offer pre-trained models. Specialized vendors focus on specific document types like contracts or invoices
- Prepare for integration — IE is most valuable when extracted data flows directly into your existing business systems (CRM, ERP, databases)
- Measure accuracy — Compare IE output against human extraction to establish accuracy baselines and identify areas for improvement
Information extraction delivers immediate, measurable ROI by automating one of the most tedious and error-prone tasks in any business: manually pulling data from documents. For CEOs, this translates to faster processing times, fewer errors, and employees freed from repetitive data entry to focus on higher-value work. The business case is straightforward — if your team spends hours each day reading documents and typing data into systems, IE can reduce that time dramatically.
For CTOs, information extraction is a practical AI implementation that integrates with existing workflows. Unlike some AI initiatives that require transforming business processes, IE can be layered onto current document handling workflows with relatively minimal disruption. Pre-built IE services from major cloud providers make deployment feasible without extensive AI expertise.
In Southeast Asian markets, where cross-border business involves documents in multiple languages, scripts, and regulatory frameworks, IE is particularly valuable. Companies processing trade documents, compliance filings, and customer records across ASEAN countries can standardize their data extraction regardless of source language or document format, creating significant operational efficiency gains.
- Identify your highest-volume document processing workflows first, as these typically offer the greatest ROI from information extraction automation
- Define clear accuracy requirements for each extraction target — financial amounts and dates may require higher accuracy thresholds than general entity extraction
- Test IE solutions on your actual documents, not just vendor demo data, as performance can vary significantly based on document quality, formatting, and language
- Plan for a human-in-the-loop review process, especially for high-stakes documents like contracts and regulatory filings where extraction errors could have serious consequences
- Consider OCR quality if your documents include scanned paper, handwritten text, or low-resolution images, as IE accuracy depends on the quality of underlying text recognition
- Budget for domain-specific model training if your documents contain specialized terminology not well represented in general-purpose IE models
- Ensure extracted data integrates smoothly with your downstream systems such as CRM, ERP, and databases to maximize operational value
Frequently Asked Questions
How accurate is information extraction compared to manual data entry?
Modern IE systems typically achieve 85 to 95 percent accuracy on well-defined extraction tasks with clean document inputs, which is comparable to or better than manual data entry, where human error rates typically range from 1 to 5 percent. However, accuracy depends heavily on document quality, language, and domain specificity. Scanned documents with OCR errors, handwritten text, and highly specialized terminology can reduce accuracy. Most businesses implement a human review step for critical data to catch any extraction errors.
Can information extraction handle documents in multiple languages?
Yes, modern IE systems support multilingual extraction, which is particularly relevant for Southeast Asian businesses dealing with documents in English, Bahasa Indonesia, Thai, Vietnamese, and other languages. Cloud-based services from Google, AWS, and Azure support dozens of languages. However, extraction accuracy may vary between languages — performance is typically highest for English and major global languages, with some Southeast Asian languages receiving less comprehensive support. Always test on your specific language mix before committing.
More Questions
Data entry automation broadly refers to any technology that reduces manual data input, including simple tools like optical character recognition and form auto-fill. Information extraction is more sophisticated — it uses AI to understand the meaning and context of text, identifying specific entities, relationships, and facts even when they appear in unstructured prose. IE can extract a contract value from a paragraph of legal text, while basic data entry automation typically works best with structured forms and templates.
Need help implementing Information Extraction?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how information extraction fits into your AI roadmap.