AI-Powered Corporate Terminology Management System

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

Comparison of Extraction Methods

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

1. Analytics—audit of current corpus, glossary requirement gathering.
2. Design—selection of methods (TF-IDF, C-value, embedding models), integration architecture.
3. Implementation—configuration of extraction, workflow interface, plugins.
4. Testing—accuracy check on a representative sample (at least 1000 terms).
5. 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.

NLP Development: Text Classification, NER, Embeddings, and Information Extraction

We often receive a task: process 50,000 support tickets — currently all manual. Dataset — 3,000 labeled examples, 12 categories, imbalance: one category occupies 40% of the sample, three at 1-2% each. Baseline accuracy — 78%. Sounds decent until you look at recall for rare classes: 0.31, 0.44, 0.28. These classes — complaints and churn threats — are most important to the business.

This is a typical NLP development project. The problem is not the algorithm but that accuracy is the wrong metric. Our experience across 30+ projects shows: we start by analyzing business metrics and only then choose the model.

Why accuracy is not the right metric for rare classes?

Accuracy ignores imbalance. If the "churn" class appears in 2% of cases, the model can predict "all good" and get 98% accuracy — but the business loses clients. Solution: F1 macro (averaged over all classes) or weighted F1. For NER — strict entity F1 (exact matches only). We guarantee: after choosing the correct metric, model quality becomes measurable and predictable.

Text Classification: From BERT to Distillation

BERT-like models are the standard for classification. ruBERT-base or ruBERT-large from DeepPavlov for Russian. multilingual-e5-large — for multiple languages in one pipeline. XLM-RoBERTa-large — a strong multilingual backbone.

Fine-tuning for classification: add a classification head on top of the [CLS] token, train for 3-5 epochs with lr=2e-5, weight decay=0.01. For imbalance — weighted CrossEntropyLoss or focal loss with gamma=2.0. Contact us — we will show a code snippet.

Imbalance case study. Dataset — 3,000 examples, imbalance 1:20. Solution: class_weight via sklearn + CrossEntropyLoss. Additionally — augmentation of rare classes via backtranslation (ru→en→ru through MarianMT). Recall for rare classes rose from 0.31 to 0.67 with a slight drop in accuracy (76%→74%). Full NLP development end-to-end took 3 weeks.

Distillation for production. BERT-large gives F1 0.89, but inference on CPU — 180ms. Distillation into DistilBERT or ruBERT-tiny2 reduces latency to 25ms with F1 0.84. Export to ONNX Runtime provides an additional 1.5-2x speedup. DistilBERT achieves 7x lower latency than BERT-large with only a 5% drop in macro F1 – a typical production trade-off.

How to choose between BERT and LLM for your task?

For most classification and extraction tasks, BERT-sized models offer the best trade-off between cost and performance. Shift to LLMs only when the task demands generation, complex reasoning, or zero-shot generalization.

NER: Named Entity Recognition

NER — extracting persons, organizations, locations, dates, amounts, document numbers. For general categories (PER, ORG, LOC), pre-trained models work well. For specialized ones (medical terms, legal concepts) — fine-tuning is needed.

Data annotation. The main cost of an NER project. For a quality model — 500-2,000 labeled sentences per entity type. Tools: Label Studio (open source) or Prodigy (by spaCy creators). IOB2 format — standard.

Architecture. Token classification on top of BERT: each token gets a label (B-PER, I-PER, O). spaCy 3.x with transformer pipeline — a convenient production choice.

Nested entities. Standard IOB models cannot handle nested entities (organization inside an address). For such tasks — span-based NER: SpanBERT or SpERT. More complex but correct.

Post-processing is mandatory. The model predicts tokens — normalized entities are needed. Date — dateparser. Amounts — regex + validation. Names — deduplication via rapidfuzz. Included in our standard delivery.

Sentiment Analysis and Opinion Mining

Binary classification positive/negative works out of the box with BERT. Complexity — aspect-based sentiment analysis (ABSA): "the restaurant has good food but terrible service." For ABSA: aspect extraction (NER) + sentiment per aspect. Joint models BERT-for-ABSA — quality on Russian data is lower due to dataset scarcity. RuSentiment, SentiRuEval — main resources.

For production with simple positive/negative/neutral: distil models are enough. Three classes, balanced dataset, 2,000+ examples — F1 macro 0.82-0.87 in 1-2 days.

Text Summarization

Extractive summarization (select sentences) — TextRank or BM25 without training. Fast, no hallucinations. Good for long documents.

Abstractive (generates new text) — seq2seq: mT5, mBART, FRED-T5, ruT5-large. For production via LLM API (GPT-4, Claude) — often the best cost/quality/speed trade-off.

Embeddings: Vector Representations of Text

Embeddings are the foundation of semantic search, deduplication, clustering, RAG. Quality critically affects downstream tasks.

Models. E5-large-v2, BGE-M3, multilingual-e5-large — strong multilingual embedders. sentence-transformers/paraphrase-multilingual-mpnet-base-v2 — fast option. For Russian: ru-en-RoSBERTa (Skoltech) performs well on semantic textual similarity.

Embedding quality evaluation uses the MTEB benchmark as standard. But top results on MTEB don't guarantee success on a domain dataset — we build domain-specific eval.

Fine-tuning embeddings. If standard models don't give the required Recall@k — contrastive learning on domain pairs with MultipleNegativesRankingLoss. How to perform this for domain data:

1. Collect 500–2,000 semantically similar pairs from your domain.
2. Apply MultipleNegativesRankingLoss with a batch size of 32–64.
3. Train for 1–3 epochs using AdamW (lr=2e-5).
4. Evaluate Recall@k on a held-out domain test set.

This approach yields a 5–15% improvement in Recall@k in practice.

Dimensionality and storage. E5-large: 1024 dim, float32 — 4KB per vector. For 10M documents — 40GB. Quantization int8 reduces to 10GB. FAISS IVF_PQ — more compact but with losses. Included in our deployment recommendations.

Information Extraction

Structured extraction is a frequent task. Examples: key contract terms, technical characteristics, dates and amounts from invoices.

1. Regex + rule-based. For INN, OGRN, amounts, dates — more reliable than neural networks. No data required.
2. NER + post-processing. For variable formats.
3. LLM with structured output. GPT‑4 / Claude with JSON schema — for complex documents. Cost: minimal per document. For 10k+ documents/day — we calculate the economics.

We guarantee a hybrid: regex/NER for typical fields + LLM for edge cases. Our guarantee is backed by years of production experience and more than 30 projects.

Work Stages

Prototype on existing data — 1-3 weeks. Production system with CI/CD — 1.5–2.5 months. Cost is calculated individually — get a consultation for a project estimate.

What's Included

• Model and pipeline architecture documentation
• Access to the model via REST API (FastAPI + ONNX)
• Client team training (2-hour webinar + Q&A)
• Accuracy guarantee on the agreed test set
• Months of post-delivery support (bug fixes, adaptation to new data)

Our Experience

Years of NLP projects from classification to RAG systems. The team includes ML engineers experienced with Hugging Face, spaCy, LangChain, MLOps. We use vLLM, Kubeflow, Weights & Biases — a production stack, not toys. Contact us to evaluate your NLP project within two days — request a free consultation on your text processing pipeline.