Why Term Extraction Matters (and Why I am Building a TEE)
Lab Entry #3
Why Term Extraction Matters
And Why I am Building a TEE for Proof of Terms
When I started reading research papers in AI, blockchain, and systems design, I kept running into a silent roadblock: terms.
Not the big flashy concepts, but the dense, domain-specific jargon packed inside every paragraph.
Terms that didn’t exist in dictionaries.
Terms that shifted meanings across disciplines.
Terms that formed the building blocks of understanding, yet were hidden in plain sight.
That’s where my obsession with term extraction began.
What Is Term Extraction?
Term extraction, also known as automatic terminology recognition (ATR), is the process of identifying important, domain-specific terms from unstructured text like research papers, documentation, or corpora.
These terms could be:
1. Single words: overfitting, latency, entropy
2. Multi-word expressions: Byzantine Fault Tolerance, zero-knowledge proof
3. Acronyms: GAN, IPFS, LLM
4. Technical verbs/adjectives: forked, tokenized, stateless
In essence, term extraction builds the linguistic map of a knowledge domain.
Why Is Term Extraction Important?
1. Accelerates Learning
If you’ve ever fallen into a research paper rabbit hole, you know how a single unknown term can block your entire flow.
Term extraction helps surface and define these concepts up front, making reading more efficient.
2. Improves Search & Indexing
Search engines, knowledge bases, and semantic systems become smarter when they understand the core terminology of a document.
This improves retrieval, tagging, and content classification.
3. Essential for Domain Adaptation in AI
Whether it’s fine-tuning a model or generating embeddings, AI systems need to know which terms matter in a given context.
Term extraction feeds downstream tasks like entity linking, QA systems, and summarization.
4. Powers Structured Knowledge
Glossaries, ontologies, taxonomies, mind maps, and knowledge graphs all begin with:
“What are the key terms in this space?”
Without term extraction, they’re just guesses.
Why I’m Building a Term Extraction Engine (TEE)
There are great research papers on term extraction.
There are even open-source libraries.
But I couldn’t find one that was:
1. Modular, domain-adaptive, and offline-first
2. Built for technical fields like blockchain, AI, and distributed systems
3. Designed for both CLI and API use
4. Capable of producing rich metadata output (term, type, score, source, context)
So I started building my own: the Term Extraction Engine (TEE).
It’s the beating heart of my long-term project: Proof of Terms
How TEE Powers the Proof of Terms (P.O.T.) Ecosystem
Proof of Terms is more than a glossary. It’s a Web3-native, domain-specific, and community-powered dictionary for emerging tech.
Here’s how term extraction fits into the workflow:
1. Upload a corpus (e.g. AI paper, blockchain whitepaper)
2. TEE extracts terms + acronyms + context
3. Each term is stored with metadata: frequency, score, type, example usage
4. Definitions can be retrieved (LLM + curation) or contributed by users
5. Glossaries are versioned, searchable, and anchored on-chain
It’s not just a tool, it’s a protocol for understanding.
What Makes TEE Different?
1. Built in Rust with NLP pipeline interop (Python)
2. Outputs rich JSON for use in APIs, UIs, agents
3. Incorporates both statistical and semantic scoring
4. Designed for both experiments and production use
5 Future roadmap includes: curation interface, LLM refinement, glossary bootstrapping
Every time you understand a complex field faster, TEE did its job.
Final Thought
In a world drowning in information, clarity is a superpower.
And clarity begins with understanding the terms.
TEE is my way of making that easier.
For myself.
For researchers.
For builders.
And eventually, for the world.
If you’re building something similar, or want to contribute to P.O.T., DM me on X @scrapychain