# Q1A · Generic NLP pipeline

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/mocks/timed-reexam-2026/reexam-2026-q1a

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/mocks/timed-reexam-2026--reexam-2026-q1a/

TEE · actual previous paper · 100 marks · 180 minutes

Question. Draw block diagram of generic NLP pipeline and explain the task associated with each block. [5 marks]

### Answer

An NLP pipeline is an ordered workflow that transforms language data into a task-specific result. The exact components depend on the application; a generic supervised pipeline is shown below.

Collect text → Clean and preprocess → Represent text → Train / apply model → Evaluate and deliver output

| Block | Task | Example for sentiment analysis |
| --- | --- | --- |
| Data acquisition | Collect representative text and any required labels | Reviews labelled positive or negative |
| Cleaning and preprocessing | Handle noise, tokenize and normalize appropriately | Remove HTML while preserving “not” |
| Text representation | Convert tokens into usable model features | TF–IDF values or contextual embeddings |
| Modeling | Learn patterns from training data and predict new labels | Train a classifier on labelled review features |
| Evaluation | Measure performance on unseen held-out data and inspect errors | Precision, recall, F1 and confusion matrix |
| Deployment and feedback | Deliver predictions and monitor changing data | Show review sentiment and monitor new product vocabulary |

Training/validation/test separation must precede fitting the learned preprocessing and feature statistics. New inputs must use the same feature mapping as training. Not every task uses every linguistic level explicitly: a modern end-to-end model may combine several operations internally.

**Yaad rakho:** Text lo → prepare karo → numbers banao → model lagao → result check aur use karo.