Natural Language Processing · TEE · mocks

Q1B · Chunking and chinking

Timed practice · Solved final paper · December 2025

TEE · actual previous paper · 100 marks · 180 minutes

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.