Enron Email Dataset — Entity Extraction
This dataset contains the results of running the Entity Extractor service over the complete Enron email corpus. The Entity Extractor is a Python-based text analysis tool that identifies and extracts structured data from unstructured text using a combination of regex pattern matching and deep learning NER models.
Dataset Overview
| Property | Value |
|---|---|
| Source | Enron Email Corpus |
| Processing | Entity Extractor (Azure ML Endpoint) |
| Split | train |
Schema
| Column | Type | Description |
|---|---|---|
Unnamed: 0 |
int64 | Row index |
file |
string | Original file path (e.g., allen-p/_sent_mail/1.) |
message |
string | Raw email content including Message-ID and headers |
extracted_entities |
list | Array of extracted entity objects |
Extracted Entity Format
Each item in the extracted_entities list contains:
{
"text": "extracted value",
"label": "entity type",
"confidence": 0.95,
"start_pos": 0,
"end_pos": 10,
"source_column": "message"
}
Supported Entity Types
The Entity Extractor identifies the following entity categories:
| Type | Code | Description |
|---|---|---|
| Email Addresses | email |
Standard email patterns including subdomains |
| Phone Numbers | phone |
Australian and international formats |
| BSB Numbers | bsb |
Australian Bank-State-Branch identifiers |
| ABN | abn |
Australian Business Numbers (with checksum validation) |
| Account Numbers | acct |
Bank account numbers with contextual matching |
| Dates | date |
Multiple formats: numeric, written, partial |
| Payment Methods | payment |
Cards, digital wallets, BNPL, transfers |
| Cryptocurrency | crypto |
8 blockchain types with cryptographic validation |
| Named Entities | ner |
People, organisations, locations (Flair NER model) |
Cryptocurrency Detection
Addresses from 8 major blockchains are detected with cryptographic validation:
- Bitcoin (BTC) — Base58Check / Bech32 checksum
- Ethereum (ETH) — EIP-55 mixed-case checksum
- Litecoin (LTC) — Base58Check / Bech32 checksum
- Monero (XMR) — Length and format validation
- Cardano (ADA) — Bech32 checksum
- Solana (SOL) — Base58 format validation
- Tron (TRX) — Base58Check checksum
- XRP (Ripple) — Base58Check checksum
Named Entity Labels (NER)
Deep learning-based extraction using Flair:
| Label | Description |
|---|---|
PERSON |
Individual names |
ORG |
Organisations and companies |
GPE |
Geopolitical entities (cities, countries) |
LOC |
Locations |
DATE |
Temporal expressions |
Use Cases
- Compliance & Fraud Detection — Identify financial identifiers and suspicious patterns
- Network Analysis — Map communication patterns via extracted emails and contacts
- Document Analysis — Parse contact information, dates, and named entities
- Cryptocurrency Investigation — Detect wallet addresses in historical communications
Accuracy Considerations
Potential False Positives
- Solana wallets: Base58 patterns may match other alphanumeric strings
- Account numbers: May match numeric sequences if contextual keywords are present
- Phone numbers: International format variations may capture unintended patterns
- Dates: Ambiguous formats (e.g., 01/02/03) cannot reliably distinguish day/month/year
Potential False Negatives
- Non-standard separators or spacing may cause valid entities to be missed
- Context-dependent entities require surrounding text that may not always be present
- Truncated data will not match expected patterns
Technical Details
- Platform: Azure Machine Learning (real-time inference endpoint)
- Compute: NVIDIA T4 GPU
- NER Model: Flair-based Named Entity Recognition
License & Attribution
The Enron email dataset is publicly available and was released by the Federal Energy Regulatory Commission during its investigation of the Enron Corporation.
Entity extraction performed using the Entity Extractor service.