Natural Language Processing · TEE · mocks

Q6A · Coreference, pronouns and Word2Vec

Timed practice · Solved final paper · December 2025

TEE · actual previous paper · 100 marks · 180 minutes

Question. i. Differentiate between co-reference resolution and pronoun resolution with suitable examples.
ii. Explain the working principle of the Continuous Bag of Words (CBOW) model and Skipgram model in Word2Vec. Illustrate how it predicts the target word from its surrounding context. [10 marks]

Answer

i. Coreference and pronoun resolution

Coreference resolution groups expressions that refer to the same entity, including names, descriptions and pronouns. Pronoun resolution identifies the referent of a pronoun; it is narrower in scope.

Expression / task	Example
Coreference cluster	“Dr Rao arrived. The surgeon opened her bag.” → {Dr Rao, the surgeon, her}
Pronoun resolution	For the same text, resolve “her” to Dr Rao.
Difference	Resolving the description “the surgeon” is coreference work even when no pronoun is involved.

ii. Word2Vec

Word2Vec learns dense word embeddings by predicting words from local context. A dense embedding is a short learned list of real numbers for a word; words used in similar contexts can obtain related vectors. CBOW and Skip-gram use opposite prediction directions.

Feature	CBOW	Skip-gram
Input	Surrounding context words	One centre word
Prediction target	The centre word	Surrounding context words
Training example	[the, cat, on, the] → sat	sat → the; sat → cat; sat → on; sat → the
Representation	Combine context embeddings, often by averaging	Use the centre-word embedding for each context prediction

Example sentence: “The cat sat on the mat.” With a radius-two window around sat, the context is [The, cat, on, the]. In CBOW, obtain the four context embeddings, average or sum them, calculate scores for vocabulary words, and train the model to assign high probability to sat. In Skip-gram, use sat as input and create four centre–context training pairs.

CBOW: nearby words combine embeddings predict centre word

Skip-gram: centre word its embedding predict nearby words

Training adjusts embedding parameters to improve prediction; the final word vectors are the learned representation. Thus the question’s phrase “predicts the target word from surrounding context” describes CBOW specifically; Skip-gram reverses that direction.

Yaad rakho: CBOW: neighbours se centre. Skip-gram: centre se neighbours.