Natural Language Processing
The foundation of language AI. Classification, extraction, sentiment, summarization — understanding text at enterprise scale.
NLP task landscape
Eight fundamental NLP tasks that power enterprise language understanding — from simple classification to complex generation.
Text Classification
Assign labels to text — spam vs. not spam, topic categorization, intent detection. The backbone of routing, filtering, and triage systems.
Named Entity Recognition
Identify and extract structured entities from text — people, organizations, dates, monetary amounts, addresses. Turns prose into queryable data.
Sentiment Analysis
Detect emotional tone and polarity in text — positive, negative, neutral, plus fine-grained emotions like frustration, delight, or urgency.
Summarization
Compress long documents into concise summaries while preserving key information. Extractive (select key sentences) or abstractive (generate new text).
Translation
Convert text between languages while preserving meaning, tone, and domain-specific terminology. Neural MT has reached near-human quality for high-resource language pairs.
Question Answering
Extract precise answers from a given context or knowledge base. Span extraction (highlight the answer) or generative (synthesize a response).
Topic Modeling
Discover latent themes across large document collections without predefined categories. Unsupervised clustering that reveals what your corpus is actually about.
Text Generation
Produce fluent, contextually appropriate text — from completing a sentence to writing entire documents. The foundation of chatbots, copilots, and content systems.
"I need to reset my password" → Intent: account_recoveryClassical vs modern NLP
Four eras of language understanding — from hand-crafted rules to zero-shot LLMs that handle any task via prompting.
Rule-Based
Hand-crafted rules, regex patterns, keyword matching, and decision trees. Linguists encoded grammar rules manually.
Statistical
TF-IDF feature extraction, SVMs, Naive Bayes, CRFs, and word2vec embeddings. Models learned patterns from labeled data.
Deep Learning
Transformer-based models pre-trained on massive corpora. BERT reads bidirectionally; fine-tuning adapts to specific tasks with small labeled datasets.
LLM-Based
Large language models handle NLP tasks via prompting — zero-shot or few-shot. No fine-tuning required for many tasks. One model, many capabilities.
Processing pipeline
From raw text to structured understanding — the five stages every NLP model executes.
Tokenization
~1msEmbedding
~2msEncoding
~10-50msTask Head
~1-5msPost-Processing
~1msSplit raw text into tokens — words, subwords, or characters. BPE and SentencePiece handle unknown words by decomposing them into known subword units.
Enterprise applications
Six production NLP patterns that transform how enterprises process, understand, and act on text data.
Customer Feedback Analysis
Process thousands of reviews, survey responses, and social mentions daily. Sentiment, topic, and urgency classification feed dashboards that product and support teams act on within hours.
Contract Review
NER extracts parties, dates, obligations, and termination clauses from legal documents. Classification flags non-standard terms and risky provisions for attorney review.
Compliance Monitoring
Continuous scanning of communications, documents, and reports for regulatory violations. Pattern matching for prohibited phrases plus semantic understanding of context.
Content Moderation
Multi-label toxicity classification across categories: hate speech, harassment, misinformation, adult content. Automated enforcement with configurable escalation thresholds.
Email Routing
Classify inbound emails by intent, urgency, and department. Auto-route to the right team, extract key details into CRM fields, and draft suggested responses for agents.
Support Ticket Classification
Multi-dimensional classification: product area, issue type, severity, and customer tier. Priority scoring combines classification confidence with account value for intelligent queue management.
Model selection
BERT-family models vs. LLMs — two fundamentally different approaches with distinct cost, latency, and flexibility profiles.
BERT-Family
Fast, cheap, domain-tunable
High-volume, latency-sensitive tasks where you have labeled training data and the task is well-defined.
LLMs
Flexible, expensive, zero-shot capable
Low-volume, diverse tasks where requirements shift frequently and you need rapid iteration without training infrastructure.
The best systems combine both — LLMs for prototyping and edge cases, fine-tuned models for high-volume hot paths. We help you find the right split for your workload and budget.
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