Natural Language Processing · TEE · papers
Q7A · Information retrieval
Solved re-exam · February 2026
Actual re-examination · 9 February 2026 · 100 marks · 3 hours
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 and define documents. Crawl or ingest documents, record metadata and remove unwanted duplicates.
- Preprocess. Tokenize and normalize using rules suitable for both documents and queries. Preserve task-relevant information.
- Build representations and an index. An inverted index maps each term to the documents containing it; a dense retrieval system stores vector representations.
- Process the query. Apply compatible tokenization, spelling handling or expansion, then obtain query features.
- Retrieve candidates. Use the index to identify potentially relevant documents efficiently.
- 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.
- Present results. Return titles, snippets and links, possibly with filtering or diversification.
- 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.