Natural Language Processing · TEE · papers
Q5b · WSD approaches and uses
2024-12-03 · final · recovered AI paper
Previous examination · complete worked answers
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.
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.