NLP Interview Questions and Answers
Ques 26. What is the role of a language model in speech recognition?
In speech recognition, a language model helps in predicting the likelihood of word sequences, improving the accuracy of transcriptions by considering context and language patterns.
Example:
A language model can aid in distinguishing between homophones (words that sound the same) based on contextual information.
Ques 27. Explain the concept of a syntactic parser in NLP.
A syntactic parser analyzes the grammatical structure of sentences, identifying the syntactic relationships between words. It helps in tasks such as parsing sentences into tree structures.
Example:
A syntactic parser can distinguish between different grammatical structures of a sentence, such as subject-verb-object.
Ques 28. What are some common challenges in named entity recognition (NER)?
Challenges in NER include handling ambiguous entities, recognizing named entities in context, and dealing with variations in entity mentions.
Example:
In biomedical texts, recognizing drug names as entities may require domain-specific knowledge and context analysis.
Ques 29. Explain the concept of a word sense disambiguation in NLP.
Word sense disambiguation aims to determine the correct meaning of a word in context when the word has multiple possible meanings.
Example:
In the sentence 'The bank is close to the river,' word sense disambiguation is needed to identify whether 'bank' refers to a financial institution or the side of a river.
Ques 30. How can you handle imbalanced datasets in sentiment analysis?
Imbalanced datasets, where one class has significantly fewer samples than another, can be addressed by techniques such as oversampling the minority class, undersampling the majority class, or using advanced algorithms like SMOTE (Synthetic Minority Over-sampling Technique).
Example:
In sentiment analysis, if there are fewer examples of negative sentiments, techniques to balance the dataset can improve model performance.
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