Natural Language Processing (NLP) System Development
An NLP system isn't one algorithm, but a pipeline of interconnected components: text preprocessing, linguistic analysis, meaning extraction, generation, or classification. System architecture is determined by task and language, not selection of one "best" library.
NLP Pipeline Components
Typical text processing pipeline includes following layers:
Normalization and Cleaning — remove HTML tags, normalize Unicode, handle special characters, normalize case. For Russian text, critical: ё/е normalization, handling hyphens in compound words.
Tokenization — split into tokens accounting for language specifics. spaCy (ru_core_news_lg) processes Russian text with morphology awareness. For LLM tasks, tokenization happens automatically (tiktoken for OpenAI).
Morphological Analysis — lemmatization, part-of-speech tagging, case determination, number. For Russian: pymorphy3, natasha, or spaCy with Russian model.
Syntactic Analysis — build dependency tree. Needed for extracting relationships between words.
Semantic Analysis — this is transformer level work: BERT, RoBERTa, their Russian counterparts (ruBERT, sbert-base-ru-mean-tokens).
Model Selection by Task
| Task | Light Solution | Heavy Solution |
|---|---|---|
| Classification (< 20 classes) | Logistic regression + TF-IDF | BERT fine-tuning |
| Classification (many classes) | FastText | DeBERTa fine-tuning |
| Entity extraction | Natasha / spaCy | BERT + CRF |
| Semantic similarity | Sentence-BERT | Cross-encoder |
| Text generation | GPT-4o-mini (API) | Fine-tuned LLaMA |
| Question-answering systems | RAG + GPT-4o-mini | Fine-tuned T5/BART |
"Light solution" often suffices for production—don't apply transformers where TF-IDF + classic ML works.
Working with Russian Text
Russian language creates additional challenges:
- Rich morphology: one word has 30+ forms. Without lemmatization, TF-IDF performs poorly
- Free word order: syntactic parsers must understand dependencies
- Mixed content: texts with Latin script, numbers, abbreviations
Recommended stack for Russian: pymorphy3 (lemmatization) + natasha (NER) + sentence-transformers with model cointegrated/rubert-tiny2 (fast embeddings) or sbert-base-ru-mean-tokens (quality).
Infrastructure and Deployment
# FastAPI service for NLP
from fastapi import FastAPI
from pydantic import BaseModel
import spacy
app = FastAPI()
nlp = spacy.load("ru_core_news_lg")
class TextRequest(BaseModel):
text: str
tasks: list[str] # ["ner", "sentiment", "keywords"]
@app.post("/analyze")
async def analyze(req: TextRequest):
doc = nlp(req.text)
result = {}
if "ner" in req.tasks:
result["entities"] = [(e.text, e.label_) for e in doc.ents]
return result
Deployment: Docker container with preloaded models. spaCy model initialization time: 2–5 seconds—critical to load at startup, not per request. GPU needed only for transformers (BERT+), spaCy sufficient on CPU.
Quality Assessment
Standard task metrics:
- Classification: precision, recall, F1 per class (important to watch per-class, not just macro)
- NER: entity-level F1 (strict—exact span + type match)
- Semantic similarity: Spearman correlation with human ratings
For production, data drift monitoring is mandatory—input text changes over time, model quality degrades without retraining.
Development Timeline
- Prototype with basic pipeline: 1–2 weeks
- Production system with one task: 3–5 weeks (including data collection, training, deployment)
- Complex NLP platform (multiple tasks): 2–4 months