# Q2A · Morpheme, lexeme and NER

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/mocks/timed-final-2025/final-2025-q2a

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/mocks/timed-final-2025--final-2025-q2a/

TEE · actual previous paper · 100 marks · 180 minutes

Question. i. Differentiate between Morpheme and Lexeme with examples.  
ii. Discuss the significance of NER in three different NLP applications. [10 marks]

### Answer

### i. Morpheme and lexeme

A **morpheme** is the smallest linguistic unit that contributes meaning or grammatical function. A **lexeme** is an abstract vocabulary item representing a family of inflected word forms.

| Basis | Morpheme | Lexeme |
| --- | --- | --- |
| Unit | A meaningful component of a word | An abstract dictionary vocabulary item |
| Examples | un-, happy, -ness; cat and plural -s | RUN covers run, runs, ran and running |
| Independence | Free morphemes can stand alone; bound morphemes cannot | A lexeme is realised through one or more word forms |
| NLP relevance | Morphological segmentation, stemming and analysis | Lemmatization and lexical meaning |

“Cats” contains two morphemes: cat + plural -s. “cat” and “cats” are forms of the lexeme CAT. “Unhappiness” can be segmented into un + happy + ness, although its spelling changes at the boundary. A morpheme and a syllable are different: syllables concern pronunciation, while morphemes concern meaning or grammar.

### ii. Named Entity Recognition in applications

**NER** locates spans that name entities and assigns types such as PERSON, ORGANIZATION, LOCATION, DATE or domain-specific categories.

| Application | Entities and example | Why NER matters |
| --- | --- | --- |
| News information extraction | “Asha joined Infosys in Pune.” → Asha/PERSON, Infosys/ORG, Pune/LOCATION | Creates searchable people–organisation–location records and supports later relation extraction |
| Healthcare document processing | Find disease, drug and dosage spans in a clinical note | Supports record indexing and medication extraction; specialist labels and validation are required |
| Customer support and commerce | Extract product names, dates and order identifiers from “My Pixel arrived damaged on Monday.” | Routes complaints, links the correct product and fills structured support fields |

NER answers “Which entity spans occur here, and what type are they?” It does not by itself establish every relation between those entities or resolve all names to unique real-world identities.

**Yaad rakho:** Morpheme = word ka meaningful piece; lexeme = word family. NER = naam dhoondho aur type do.