From Raw Text to Ready Glossaries: ML+LLM-Powered Term Extraction at Scale

Term Extractor is an ML- and LLM-driven tool that automatically mines candidate glossary terms from real product content (e.g., PRDs, UI copy, support text) and prepares them for human validation. It ingests plain-text documents, identifies single and multi-word terms, and scores each candidate by frequency, relevance, and linguistic salience using a combination of statistical keywording (YAKE), semantic similarity (BERT-based sentence transformers), and part-of-speech patterns from spaCy. For every shortlisted term, the system also generates a context-aware definition using LLM models, so reviewers see not just a word list, but a ranked, ready-to-curate glossary dataset in CSV or UI form. 

Key Takeaways 

1. End-to-End Term Mining Workflow See how we go from a raw .txt / .doc or any other file type (one sentence per line) to a ranked list of candidate terms with parts of speech, frequencies, relevance scores, and contextual definitions ready for glossary ingestion. 

2. Under the Hood: Tech Stack and Ranking Logic Understand how the pipeline combines Flask, spaCy, YAKE, sentence-transformers (all-mpnet-base-v2), pandas, and OpenAI APIs to parse text, extract candidates, compute embeddings, and blend frequency, semantic similarity, and POS-based weights into a single importance score per term. 

3. Context-Aware Definitions, Not Just Keywords Learn how we use the full document as context to draft concise, machine-generated definitions for each high-value term, speeding up human review while keeping humans in the loop for final approval and glossary integration. 

4. Impact on Quality, Turnaround Time, and Cost Review before-and-after metrics showing how automated term mining dramatically reduces manual effort per term, improves terminology coverage and consistency, and enables “touchless” term mining workflows that scale across products and domains while still fitting into existing term management and localization pipelines. 

Who should attend

● Localization and globalization program managers 

● Terminologists, reviewers, and in-house linguists 

● Localization engineers and NLP/ML practitioners working on language tooling 

● Content designers, UX writers, and technical writers supporting global products