Question: Part ISentence completion using N$-$gram:Recommend the top $3$words to complete the given sentence using N$-$gram language model. The goal is to demonstrate the relevance of recommended words based on the occurrence of Bigram within the corpus. Use all the instances in the dataset as a training corpus.Test Sentence: Operating profitPart IIPerform the below

Part I

Sentence completion using N

$-$

gram:

Recommend the top

$3$

$$words to complete the given sentence using N

$-$

gram language model. The goal is to demonstrate the relevance of recommended words based on the occurrence of Bigram within the corpus. Use all the instances in the dataset as a training corpus.

Test Sentence: Operating profit

Part II

Perform the below sequential tasks on the given dataset.

i

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$$Text Preprocessing:

Tokenization

Lowercasing

Stop Words Removal

Stemming

Lemmatization

ii

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$$Feature Extraction:

$$ Use the pre

$-$

processed data from previous step and implement the below vectorization methods to extract features.

Word Embedding using TD

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IDF

iii

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$$Similarity Analysis:

Use the vectorized representation from previous step and implement a method to identify and print the names of top two similar documents that exhibit significant similarity. Justify your choice of similarity metric and feature design. Visualize a subset of vector embedding in

$2$

D semantic space suitable for this use case. HINT:

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Use PCA for Dimensionality reduction

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