Entity Extraction: How AI Finds Meaning in Your Notes

When you write a note, you're capturing more than text. You're documenting people, places, projects, deadlines, and ideas. Entity extraction is how Lexic identifies these elements automatically, turning raw text into structured, connected knowledge.

What Are Entities?

In natural language processing, an "entity" is a specific thing mentioned in text. The classic categories are:

• People: Names like "Sarah Chen" or "Dr. Martinez"
• Organizations: Companies, teams, departments
• Locations: Cities, offices, addresses
• Dates and times: "Next Tuesday," "Q4 2024," "3pm EST"
• Monetary values: "$50,000 budget," "15% increase"

But for knowledge management, we care about additional types:

• Projects: Specific initiatives you're tracking
• Concepts: Ideas, methodologies, frameworks
• Products: Tools, software, systems you use
• Actions: Tasks, decisions, commitments

How Entity Extraction Works

Modern entity extraction uses language models trained on massive text datasets. The process works roughly like this:

1. Tokenization: The text is broken into processable chunks—words and subwords that the model can analyze.

2. Context analysis: The model examines each token in context. "Apple" means something different in "Apple announced" versus "apple pie recipe."

3. Entity classification: Identified entities are categorized by type. Is this a person, organization, or something else?

4. Coreference resolution: Multiple mentions of the same entity are linked. "Sarah," "she," and "the team lead" might all refer to the same person.

5. Relationship extraction: Beyond individual entities, the model identifies how they relate. "Sarah leads Project Alpha" captures both entities and their connection.

Why Extraction Matters for Notes

Without entity extraction, your notes are just text. With it, they become data that can be queried, connected, and analyzed:

Automatic linking: When you mention Project Alpha in a new note, it automatically connects to your existing Project Alpha notes. No manual tagging required.

People pages: All mentions of a person aggregate into a single view—every meeting, every project, every context where they appear.

Timeline views: Date entities enable timeline visualizations. See what was discussed or decided on any given date.

Action tracking: Extracted tasks and commitments can feed into task management views, even if you didn't explicitly create a to-do item.

The Challenge of Accuracy

Entity extraction isn't perfect. Common challenges include:

Ambiguous names: "Jordan" could be a person, a country, or part of "Air Jordan." Context helps, but ambiguity remains.

Custom terminology: Your organization has its own project names, acronyms, and jargon that generic models don't recognize.

Evolving references: People change roles, projects change names, and language evolves. Yesterday's "the new system" is today's "Project Phoenix."

The best extraction systems learn from corrections and adapt to your specific usage patterns.

Extraction in Lexic

Lexic's entity extraction runs automatically on every note you create or edit. Here's how we approach it:

Multi-pass processing: We run initial extraction, then refine based on your existing knowledge graph. If "Sarah" appears frequently connected to "Engineering," new mentions of "Sarah" default to that context.

Transparent costs: Before processing a long document, you see exactly how many words will be analyzed and what it costs. Short notes extract instantly; longer documents give you a cost preview.

Manual override: Our AI makes suggestions, but you have final say. Mark incorrect extractions, merge duplicate entities, or add connections the AI missed.

Continuous improvement: Your corrections improve future extraction. The system learns your terminology, your team's names, your project conventions.

Beyond Simple Extraction

Entity extraction is the foundation, but the real power comes from what you build on it:

Relationship graphs: Entities and their connections form a knowledge graph you can explore visually and query naturally.

Smart search: Find notes not just by keywords but by the entities they contain and the relationships they describe.

Automated summaries: With structured entity data, generating meeting summaries, project overviews, and status reports becomes possible.

The Future of Understanding

Entity extraction is rapidly improving. Tomorrow's systems will understand nuance, track entity evolution over time, and extract increasingly subtle relationships.

For knowledge workers, this means your notes become more valuable over time. Today's raw capture becomes tomorrow's structured intelligence—automatically.