Q121
Q121 What is the primary goal of a language model in NLP?
To generate grammatical rules
To predict the next word in a sequence
To tokenize text
To remove stopwords
Q122
Q122 Which type of language model uses probabilities based on word sequences?
Neural language models
Statistical language models
Transformer-based models
Contextual embeddings
Q123
Q123 How do neural language models improve over statistical models?
They use fixed word probabilities
They capture context through embeddings
They don’t require training
They process sequentially
Q124
Q124 What is the main limitation of n-gram language models?
Overfitting
Memory consumption
Lack of long-range dependencies
Slow training
Q125
Q125 Which Python library offers tools to create n-gram models for language modeling?
nltk
spaCy
Hugging Face
gensim
Q126
Q126 How do you implement a unigram language model using nltk?
Use FreqDist to calculate word probabilities
Tokenize text
Both
Neither
Q127
Q127 A language model performs poorly due to rare words in the training data. What could you do?
Use smoothing techniques
Increase dataset size
Remove rare words
Ignore them completely
Q128
Q128 A transformer-based language model generates repetitive sentences. What can fix this?
Reduce model layers
Use beam search
Increase dataset size
Apply n-gram penalty
Q129
Q129 What is the main purpose of topic modeling in NLP?
To generate embeddings
To identify latent topics in text
To classify text
To predict word sequences
Q130
Q130 How does LDA (Latent Dirichlet Allocation) model topics?
By clustering embeddings
By using a probabilistic generative model
By matrix factorization
By neural networks
Q131
Q131 What is the key limitation of LDA in topic modeling?
Handles large corpora poorly
Cannot capture word order
Requires labeled data
Cannot handle stopwords
Q132
Q132 How does NMF (Non-negative Matrix Factorization) differ from LDA?
NMF uses embeddings
NMF relies on matrix factorization
NMF captures word order
NMF handles labeled data
Q133
Q133 Which library provides an easy-to-use implementation of LDA for topic modeling?
spaCy
gensim
TextBlob
Hugging Face
Q134
Q134 How do you visualize LDA topics using pyLDAvis?
Use pyLDAvis.gensim_models.prepare()
Use plot()
Use pyLDAvis.show()
Use pyLDAvis.plot()
Q135
Q135 An LDA model produces incoherent topics. What should you adjust?
Increase dataset size
Use fewer topics
Use more iterations
Reduce vocabulary size
Q136
Q136 A topic model generates overlapping topics. How can you address this issue?
Reduce dataset size
Increase topic separation parameter
Use fewer topics
Apply NMF instead of LDA
Q137
Q137 What is the primary function of a chatbot in conversational AI?
To classify text
To answer user queries
To generate embeddings
To predict next words
Q138
Q138 What technique allows chatbots to maintain context in a conversation?
Tokenization
Contextual embeddings
Sequence-to-sequence models
Dialogue management
Q139
Q139 Which evaluation metric is best suited for measuring the effectiveness of chatbots?
BLEU
F1 Score
WER
Dialog Success Rate
Q140
Q140 Which Python library provides tools for building rule-based chatbots?
spaCy
nltk
ChatterBot
TextBlob
Q141
Q141 How do you integrate a transformer-based language model like GPT into a chatbot?
Use rule-based replies
Fine-tune the model
Apply TF-IDF
Use a Bag-of-Words approach
Q142
Q142 A chatbot often misinterprets user intent. What can improve this?
Reduce vocabulary size
Train an intent classifier
Disable embeddings
Apply stemming
Q143
Q143 A conversational AI system generates irrelevant responses. What should you adjust?
Increase model size
Add context tracking
Use fewer intents
Reduce training epochs
Q144
Q144 What is a common application of NLP in healthcare?
Sentiment analysis
Named entity recognition
Clinical text analysis
Text summarization
Q145
Q145 How does sentiment analysis enhance customer feedback analysis in e-commerce?
By providing word embeddings
By quantifying customer satisfaction
By classifying intents
By generating product descriptions
Q146
Q146 What is the primary challenge of applying NLP to legal document analysis?
Lack of training data
Complex language structure
Short text length
Frequent spelling errors
Q147
Q147 Which Python library is commonly used for analyzing sentiment in social media datasets?
nltk
TextBlob
VADER
spaCy
Q148
Q148 How do you implement topic modeling for news articles using LDA in gensim?
gensim.topic_model()
LdaModel()
gensim.news_topic()
gensim.topics()
Q149
Q149 An NLP system fails to extract entities from noisy customer support logs. What should you do?
Use a pre-trained model
Fine-tune with domain data
Ignore noisy data
Apply stemming
Q150
Q150 A spam detection system flags non-spam emails as spam. What could you adjust?
Add more training data
Ignore stopwords
Reduce model size
Apply lemmatization
