Named Entity Extraction and Large-Scale Document Analysis

What this service covers

• Text extraction from PDFs, Word files and web pages
• Named entity recognition: people, organisations, places, dates, amounts
• Custom entities such as programme names, species, diseases or product codes
• Relationship extraction, for example who works with whom
• Document similarity, clustering and search
• Summaries of long documents and collections
• Output as spreadsheets, databases or network graphs

From unstructured to structured

Valuable information often sits inside long documents. Entity extraction finds the items you care about and writes them into a table, so you can count, filter, map and compare. A policy review, for instance, can be turned into a table of institutions, dates and commitments across hundreds of documents.

Accuracy and review

Off-the-shelf extraction works well for common entities but can miss domain-specific terms. We test on a sample, add custom rules or trained models when needed and report accuracy. Scanned documents may need OCR first, and we flag where poor scans limit what can be extracted.

Document-level analysis

Beyond entities, we cluster similar documents, trace how language changes over time and find passages related to a topic. This links to topic modelling and keyword analysis, and to content analysis when you need systematic coding.

Uses

Common uses are literature and policy reviews, due diligence, grant and report analysis and evaluation document reviews. See also text classification.

How the work runs

1. You share the documents and describe what to extract.
2. We build and test the extraction on a sample.
3. We process the full collection and check quality.
4. You receive structured data and a summary.

What you receive

• A structured table of extracted entities and facts
• Source references back to the page or document
• Document clusters or similarity results
• Quality check results
• Summary and visualisations