NLP_B2_W7

Which of the following is an example of a language model that uses a probabilistic approach?

• Rule-based model

• Hidden Markov model (HMM)

• Decision tree

• Convolutional neural network (CNN)

Which of the following is a limitation of HMMs?

• They may not capture long-range dependencies well

• They require minimal training data

• They are easy to implement

• They can model sequences of data

What assumption does an HMM make regarding the Markov property?

• Observations are independent

• There are no hidden states

• The next state depends only on the current state

• The next state depends on all previous states

In an HMM, what do the "hidden states" represent?

• The final output of the model

• The input data

• The underlying processes that generate observations

• The visible outcomes in a sequence

Which of the following techniques can improve the accuracy of POS tagging?

• Reducing the dataset size

• Ignoring punctuation

• Using context and surrounding words

• Increasing the number of stop words

What challenge does POS tagging face with the word "lead" in the sentence "He will lead the team"?

• Long sentences

• None of the above

• Lack of training data

• Ambiguity due to multiple meanings

What is the primary purpose of POS tagging in NLP?

• To assign grammatical categories to words in a sentence

• To summarize the main ideas of a document

• To identify the sentiment of a text

• To translate text into different languages

Which technique is often used to create chunking rules?

• Unsupervised learning

• Reinforcement learning

• Rule-based systems

• Supervised learning

What is chunking in NLP?

• The summarization of documents

• The conversion of text into numerical data

• The identification of phrases or groups of words in a sentence

• The process of breaking text into sentences

Which method is used to deal with the problem of out-of-vocabulary words in language modeling?

• Named entity recognition

• Byte Pair Encoding (BPE)

• WordNet

• Word sense disambiguation