NCA-GENL Question 164
Select 2You are tasked with building a pipeline to process and analyze large amounts of text data for a customer service chatbot. The pipeline must include tokenization, named entity recognition (NER), vector similarity search for intent matching, and numerical computations for ranking responses. Which combination of Python libraries would best fulfill these requirements?
- A
spaCy for tokenization and named entity recognition, and a vector database like FAISS for similarity search
- B
NumPy for tokenization and named entity recognition, and FAISS for similarity search
- C
spaCy for tokenization and named entity recognition, and NumPy for numerical computations
- D
A vector database like FAISS for tokenization and named entity recognition, and NumPy for similarity search
- E
spaCy for tokenization and NER, FAISS for similarity search, and NumPy for numerical computations
Show answer and explanation
Correct answers: A, E
Explanation
To build a pipeline for analyzing text data for a customer service chatbot, you need to leverage libraries tailored to specific tasks. spaCy is well-suited for tokenization and named entity recognition, FAISS is optimized for vector similarity search, and NumPy is ideal for numerical computations like ranking responses. Combining these tools ensures the pipeline effectively handles all required functionalities.
- A. Correct.
This is a correct option because spaCy is a powerful library for natural language processing tasks like tokenization and named entity recognition, and FAISS is a specialized library for efficient vector similarity search.
- B. Incorrect.
This is incorrect because NumPy is not designed for tasks like tokenization or named entity recognition. It is a numerical computation library.
- C. Incorrect.
This is partially correct because spaCy can handle tokenization and NER, but NumPy cannot perform vector similarity searches, which are required for this scenario.
- D. Incorrect.
This is incorrect because FAISS is not designed for tokenization or named entity recognition, and NumPy cannot handle vector similarity search.
- E. Correct.
This is a correct option because it combines spaCy for tokenization and NER, FAISS for similarity search, and NumPy for numerical computations, covering all the requirements of the pipeline.