| # 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: |
|
|
| ```json |
| { |
| "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. |
