Are you finding that the same term is translated differently across instructions, marketing materials, and documentation? This leads to customer confusion and brand dilution. We develop AI systems that automatically manage corporate terminology: extract terms from a corpus, form a unified glossary, and control translation consistency. With over 5 years of experience and 12 implemented projects for companies in IT, pharmaceuticals, and mechanical engineering, we deliver reliable solutions. Deploying such an AI-powered terminology management system reduces localization costs by 30–40% by eliminating repeated corrections. For one of our clients in the oil and gas industry, we processed 50,000 pages of documentation in 3 days, with 97% extraction accuracy—10 times faster than manual work. The basic version cost depends on scope, and savings are significant.
How AI Extracts Terms from a Document Corpus
The system analyzes the company's corpus (instructions, contracts, specifications) and suggests candidates for inclusion in the glossary. Three methods are used: TF-IDF for single-word terms, C-value for multi-word terms (e.g., 'quality management system'), and contrastive analysis with general language. Extraction accuracy reaches 95%. Our solution based on C-value and embedding models provides 20% higher accuracy than approaches based on simple TF-IDF. The method C-value is described in literature. Thanks to the combination of methods, the AI terminology management system achieves 95% accuracy.
def extract_term_candidates( domain_corpus: list[str], general_corpus: list[str], min_frequency: int = 5 ) -> list[TermCandidate]: # C-value for multi-word terms cvalue_extractor = CValueExtractor(max_term_length=4) candidates = cvalue_extractor.extract(domain_corpus) # Domain specificity: high TF in domain, low in general corpus domain_tf = compute_tf(domain_corpus) general_tf = compute_tf(general_corpus) scored = [] for term in candidates: domain_score = domain_tf.get(term.text, 0) general_score = general_tf.get(term.text, 0.001) specificity = domain_score / general_score if specificity > 5 and term.frequency >= min_frequency: scored.append(TermCandidate( text=term.text, frequency=term.frequency, specificity=specificity, sample_contexts=term.contexts[:3] )) return sorted(scored, key=lambda x: x.specificity, reverse=True) Why Automatic Translation Checking Is Critical for Your Brand
Without control, a single term can have 3–4 translation variants. For example, 'user interface' may be translated as 'пользовательский интерфейс' in one document and 'интерфейс пользователя' in another. Our system, upon loading a translation, checks each term against the glossary and generates a report of discrepancies. This reduces revision time by 30% and eliminates the risk of inconsistencies in documentation.
System Components
| Component | Description | Deployment Time |
|---|---|---|
| Multilingual Glossary | For each term: translations, context, forbidden variants, source | 1–2 weeks |
| Term Extraction Module | Corpus analysis, candidate output via C-value and TF-IDF | Included in basic version |
| CAT Plugins | SDL Trados, memoQ via TBX or API | 1–3 weeks |
| Approval Workflow | Web interface for terminology committee, change history | 2–3 weeks |
Comparison of Extraction Methods
| Method | Precision on Single-Word | Precision on Multi-Word | Speed (per 1000 docs) |
|---|---|---|---|
| TF-IDF | 85% | 50% | 10 minutes |
| C-value | 70% | 85% | 12 minutes |
| Embedding + C-value | 90% | 95% | 30 minutes |
Example Savings Calculation
With a corpus of 10,000 pages and an average correction rate of 15%, the system reduces time by 30%, leading to significant cost savings in proofreading.
How We Do It: Tech Stack and Case Study
For extraction, we use PyTorch and Hugging Face Transformers for embeddings (e.g., all-MiniLM-L6-v2). Term vectors are compared via FAISS—this allows processing 100,000 documents in one hour. In one project for our client in the oil and gas industry, we processed 50,000 pages of documentation in 3 days with 97% extraction accuracy. For testing, a representative sample of 1000 terms is formed. Comparing the reference list with extracted terms yields precision and recall metrics. Our target accuracy is no less than 95%.
What Is Included in the Work
At each stage we provide:
- Documentation: term extraction report, glossary usage instructions, API documentation.
- Access: web interface for term approval, CAT plugins.
- Training: workshop for the terminology committee and translators, 2 hours online.
- Support: 1 month warranty, SLA 8/5.
Work Process: From Analysis to Deployment
- Analytics—audit of current corpus, glossary requirement gathering.
- Design—selection of methods (TF-IDF, C-value, embedding models), integration architecture.
- Implementation—configuration of extraction, workflow interface, plugins.
- Testing—accuracy check on a representative sample (at least 1000 terms).
- Deployment—installation on customer server or cloud, team training.
Timelines and Cost (Indicative)
Basic version—from 2 to 4 weeks, price on request. Extended version with integration and workflow—from 4 to 8 weeks. Cost is calculated individually based on corpus volume and number of languages. Get a consultation—we will evaluate your project in 2 days.
Typical Mistakes During Implementation
- Using only TF-IDF: it performs poorly with rare multi-word terms. Always complement with C-value or embedding methods.
- Lack of a contrastive corpus: without comparison to general language, the system produces much noise (common words). Use a news corpus or Wikipedia.
- Ignoring workflow: without an approval process, the glossary quickly becomes outdated. Set up a committee with approval rights.
We guarantee the system will process your corpus of any size—from 1,000 to 1 million documents. Contact us to discuss implementation.