FREE Natural Language Processing MCQ Questions and Answers
Pick one of the following applications for NLP.
Explanation:
NLP can be useful in Market Basket Analysis. By using NLP techniques to analyze customer reviews, feedback, and social media data, businesses can gain insights into customer preferences and behavior, identify trends, and uncover potential opportunities for cross-selling or up-selling. NLP can also extract and classify product attributes and features from customer reviews, which can be used to improve product development and marketing strategies.
What does Natural Language Processing (NLP) entail?
Explanation:
The field of Natural Language Processing (NLP) is a multidisciplinary field that combines elements of linguistics, computer science, and artificial intelligence (AI).
Translation by machine produces _________.
The primary difficulty with NLP is ________.
Explanation:
One of the main challenges of NLP (Natural Language Processing) is handling the ambiguity of natural language sentences. Natural language is often ambiguous, meaning that the same sentence can have multiple meanings depending on the context in which it is used. Resolving this ambiguity is a major challenge in NLP and requires developing sophisticated algorithms and models that can accurately interpret the meaning of natural language text.
NLP is interested in how computers and human (natural) languages interact.
Explanation:
NLP (Natural Language Processing) is a field of study focusing on interactions between computers and human (natural) languages. NLP aims to develop algorithms and models that can process, understand, and generate natural language in a useful and meaningful way for humans.
Machine learning, particularly statistical machine learning, is the foundation of contemporary NLP algorithms.
Explanation:
Modern NLP algorithms heavily rely on machine learning techniques, particularly statistical machine learning, to analyze and understand natural language text. Machine learning algorithms are trained on large amounts of annotated text data, which allows them to learn patterns and relationships in the language and make predictions about new, unseen text. This approach has succeeded in various NLP tasks, including text classification, sentiment analysis, language translation, and more.