# Q1B · Chunking and chinking

Subject: Natural Language Processing

Exam: M2

Reader: https://deep-learning-study-room.vercel.app/nlp/#m2/papers/final-2025/final-2025-q1b

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/m2/papers/final-2025--final-2025-q1b/

M2-relevant questions from an actual TEE paper · original marks retained for longer practice

Question. Explain the concepts of chunking and chinking in Natural Language Processing (NLP). Discuss how Part-of-Speech (POS) tagging supports these processes, and illustrate your explanation with a suitable example. [5 marks]

### Answer

**Chunking** groups adjacent tagged words into shallow phrases, such as noun phrases, without building a complete sentence parse. **Chinking** removes a specified sequence of tags or words from a chunk, splitting a larger candidate chunk when required.

### POS tags provide the pattern

A POS tag describes a grammatical category: DT = determiner, JJ = adjective, NN = singular noun, VBD = past-tense verb and IN = preposition. A noun-phrase rule can be written as `NP: {<DT>?<JJ>*<NN>+}`: an optional determiner, zero or more adjectives, followed by one or more nouns.

### Worked example

| Word | The | small | cat | sat | on | the | mat |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Tag | DT | JJ | NN | VBD | IN | DT | NN |

Applying the NP rule gives **[The small cat] sat on [the mat]**. The first chunk contains DT–JJ–NN; the second contains DT–NN. “sat on” is not included because it does not match the noun-phrase rule.

For chinking, begin with one broad chunk: **[The small cat sat on the mat]**. Remove the VBD and IN sequence “sat on”, for example with `}<VBD|IN>+{`. The result is **[The small cat] sat on [the mat]**.

POS tagging therefore supplies grammatical labels; chunking uses labels to select phrases, while chinking uses labels to exclude unwanted material. Incorrect tags can lead to incorrect chunks.

**Yaad rakho:** Chunking = phrase ko jodo; chinking = beech ka unwanted part nikalo.