# Q7A · Information retrieval

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/papers/reexam-2026/reexam-2026-q7a

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q7a/

Actual re-examination · 9 February 2026 · 100 marks · 3 hours

Question. i. Explain the concept of Information Retrieval, highlighting its role in Recommender Systems and Search Engines.  
ii. Discuss the various steps involved in the Information Retrieval process. [10 marks]

### Answer

### i. Concept and roles

**Information Retrieval (IR)** finds and ranks documents or other information objects relevant to a user’s information need. A query is the user’s expression of that need. IR typically returns a ranked set of items; information extraction instead identifies structured facts or fields inside those items.

| Application | Role of IR | Example |
| --- | --- | --- |
| Search engine | Matches a submitted query against a large indexed collection and ranks candidate pages | A search for “symptoms of dehydration” returns relevant health pages |
| Recommender system | Retrieves candidate items using a user profile, recent activity or a content representation; a later model may personalize ranking | Retrieve articles similar to topics the user reads |

Retrieval is a component of many recommenders; recommendation also involves preferences, feedback, novelty and business constraints. A recommender need not wait for an explicit textual query.

### ii. Retrieval process

Collect → Preprocess → Index → Process query → Retrieve candidates → Rank → Evaluate

1. **Collect and define documents.** Crawl or ingest documents, record metadata and remove unwanted duplicates.
2. **Preprocess.** Tokenize and normalize using rules suitable for both documents and queries. Preserve task-relevant information.
3. **Build representations and an index.** An inverted index maps each term to the documents containing it; a dense retrieval system stores vector representations.
4. **Process the query.** Apply compatible tokenization, spelling handling or expansion, then obtain query features.
5. **Retrieve candidates.** Use the index to identify potentially relevant documents efficiently.
6. **Score and rank.** A lexical method may use TF–IDF/BM25-style evidence; a semantic method compares representations. Rank scores estimate relevance rather than proving correctness.
7. **Present results.** Return titles, snippets and links, possibly with filtering or diversification.
8. **Evaluate and update.** Use relevance judgments and measures such as precision, recall or ranking metrics. Refresh the index as documents change.

Example: for “solar energy storage”, an inverted index retrieves documents containing those terms; a ranking function rewards useful matches. A semantic system may also retrieve relevant material using related terms such as batteries.

**Yaad rakho:** IR mein relevant document dhoondho aur rank karo; extraction mein document ke andar facts nikalo.