Virtual Labs

Building Chunker

What is the main goal of chunking in Natural Language Processing?

a: To divide a sentence into non-overlapping, meaningful groups of words Explanation

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b: To translate text from one language to another Explanation

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c: To generate random sentences Explanation

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d: To count the number of words in a sentence Explanation

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Which of the following is an example of a chunk in the sentence: 'The quick brown fox jumps'?

a: The quick brown fox Explanation

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b: jumps Explanation

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c: The Explanation

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d: quick brown Explanation

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Which machine learning models are commonly used for chunking in this experiment?

a: Hidden Markov Model (HMM) Explanation

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b: Conditional Random Field (CRF) Explanation

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c: Both HMM and CRF Explanation

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d: Neural Networks only Explanation

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What is the effect of increasing the size of the training corpus on chunking accuracy?

a: Accuracy generally increases Explanation

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b: Accuracy generally decreases Explanation

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c: Accuracy stays the same Explanation

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d: There is no effect Explanation

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Which feature set is likely to give the best chunking accuracy?

a: Only lexicon Explanation

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b: Only part-of-speech (POS) Explanation

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c: Both lexicon and POS Explanation

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d: Random features Explanation

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Why might a CRF model outperform an HMM model for chunking?

a: CRF can use more complex and global features Explanation

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b: CRF ignores the input sequence Explanation

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c: HMM always overfits Explanation

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d: HMM cannot be used for chunking Explanation

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What is a key difference between chunking and full parsing?

a: Chunking finds non-overlapping groups, full parsing finds complete syntactic structure Explanation

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b: Chunking is slower than full parsing Explanation

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c: Full parsing ignores word order Explanation

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d: Chunking is only used for English Explanation

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Suppose your chunker is not performing well. Which of the following is LEAST likely to help improve its accuracy?

a: Adding more labeled training data Explanation

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b: Using richer features (e.g., combining lexicon and POS) Explanation

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c: Randomly shuffling the labels in the training data Explanation

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d: Switching from HMM to CRF Explanation

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In the context of chunking, what does a 'feature' refer to?

a: A property or attribute of a word or its context used by the model Explanation

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b: A type of chunk Explanation

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c: A sentence in the training data Explanation

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d: A random number Explanation

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Which of the following best describes a 'chunk' in NLP?

a: A non-overlapping, non-recursive group of words forming a meaningful unit Explanation

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b: A single word only Explanation

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c: A random group of words Explanation

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d: A full syntactic tree Explanation

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