# Q1a · WSD and pronoun resolution

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/papers/legacy-f23/q1a

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1a/

Previous examination · complete worked answers

**Question. Define Word Sense Disambiguation and Pronoun Resolution. Give an example of each.** [5 marks]

### Answer

**Word sense disambiguation (WSD) is the task of selecting the intended sense of an ambiguous word in its context from a set of possible senses.** The output is a sense label, not a different replacement word.

Input: “He grilled the bass.”

→

Candidate senses: fish / low sound

→

Context: grilled

→

Output: fish sense

The **target word** is the ambiguous word. The **context** is the surrounding evidence. The **sense inventory** lists candidate meanings, often using WordNet. A **feature** is a usable clue: nearby word, position, part-of-speech tag or a phrase.

| Approach | What it needs | How it decides | Limitation |
| --- | --- | --- | --- |
| Knowledge based | Dictionary, thesaurus or semantic graph | Definition overlap, category evidence or graph scores | Short definitions and missing knowledge |
| Supervised | Examples already labelled with senses | Learns context → sense mapping | Labelling cost, limited rare-sense examples |
| Semi-supervised | Small labelled set plus unlabelled data | Bootstraps from reliable seed examples | Early errors can spread |
| Unsupervised sense induction | Unlabelled contextual occurrences | Clusters similar contexts | Clusters are not automatically named dictionary senses |
| Neural/contextual | Learned word/context representations | Predicts or compares a context representation | Needs suitable data/model and evaluation |

**Bag-of-words features** record nearby words while ignoring exact positions. **Collocational features** preserve a positional phrase such as “river bank”, “bank account”, “chocolate bar” or “bar exam”.

### Why WSD matters

Machine translation needs the correct target-language meaning; information retrieval should distinguish river-bank travel from banking services; text-to-speech may need different pronunciations for “bass”; information extraction should avoid connecting entities through the wrong relation.

WSD: same spelling ke options mein context dekh ke sahi meaning select.

**Pronoun resolution** identifies the antecedent referred to by a pronoun. In “Rita arrived. She smiled”, she refers to Rita. WSD chooses a word meaning (bank as finance or river edge); pronoun resolution chooses a discourse entity.