Natural Language Processing · TEE · mocks

Q5A · Why TF–IDF is useful

Timed practice · Solved re-exam · February 2026

TEE · actual previous paper · 100 marks · 180 minutes

Question. Justify with an appropriate example the importance of TF-IDF technique in the document representation and information retrieval task. [10 marks]

Answer

TF–IDF represents each document using weights that combine within-document term frequency with how unusual a term is across the collection. It supports information retrieval by giving larger weight to terms that are frequent in a document but not present in every document.

Given

Use three short documents: D₁ = “solar energy solar”; D₂ = “wind energy”; D₃ = “energy policy”. Use raw-count TF and natural-log unsmoothed IDF. N = 3.

To find

A TF–IDF representation illustrating why distinctive terms help retrieve relevant documents.

Formula: which one and why

\mathrm{TFIDF}(t,d)=C(t,d)\ln\frac{N}{\mathrm{df}(t)}

C counts occurrences within a document; df counts documents containing the term. The logarithm reduces the scale of very large rarity ratios.

Solution

Step 1. Count term occurrences

Term	D₁	D₂	D₃	df
solar	2	0	0	1
energy	1	1	1	3
wind	0	1	0	1
policy	0	0	1	1

Step 2. Calculate IDF

\mathrm{IDF}(solar)=\mathrm{IDF}(wind)=\mathrm{IDF}(policy)=\ln3\approx1.098612

\mathrm{IDF}(energy)=\ln(3/3)=0

Step 3. Multiply each count by its IDF

Term	D₁ weight	D₂ weight	D₃ weight
solar	2 × 1.098612 = 2.197224	0	0
energy	0	0	0
wind	0	1.098612	0
policy	0	0	1.098612

Step 4. Connect the representation to retrieval

A query containing “solar” matches D₁’s high solar coordinate. A query containing “wind” matches D₂. “energy” alone cannot distinguish these documents under this unsmoothed collection because it occurs in all three. A retrieval system can compare query and document vectors, commonly by cosine similarity.

Final answer

TF–IDF preserves distinctive lexical evidence and reduces dominance by collection-wide frequent terms. D₁ is most relevant to a solar query under these features.

Yaad rakho: Document mein frequent + collection mein rare = high TF–IDF.

Strengths and limits

The representation is sparse, interpretable and useful as a fast baseline. However, basic TF–IDF ignores word order and does not recognise synonyms or multiple word senses automatically. Its weights depend on the collection and on the chosen TF/IDF convention. Fit IDF on training or indexed documents, not on a hidden test set.