NCA-GENL Question 40
Select 2A team is building a text classification pipeline using Python natural language processing tools. They need to preprocess text data, create word embeddings, and store/query the embeddings efficiently. Which combination of tools would best suit these tasks?
- A
spaCy for text preprocessing
- B
NumPy for creating and storing word embeddings
- C
vector databases like Pinecone for storing and querying embeddings
- D
spaCy for querying word embeddings
- E
NumPy for storing large-scale embeddings efficiently
Show answer and explanation
Correct answers: A, C
Explanation
The best approach combines spaCy for preprocessing and a vector database like Pinecone for storing and querying embeddings. spaCy provides robust tools for text preprocessing, and Pinecone is purpose-built for handling large-scale embeddings efficiently. While NumPy is useful for numerical operations, it is not ideal for tasks requiring efficient storage and retrieval at scale.
- A. Correct.
spaCy is well-suited for text preprocessing, such as tokenization, lemmatization, and named entity recognition, making it a correct choice for the preprocessing step.
- B. Incorrect.
While NumPy is useful for numerical computations and creating small-scale word embeddings, it is not designed for efficient storage or querying of embeddings, especially at scale.
- C. Correct.
Vector databases like Pinecone are specifically optimized for storing and querying large-scale embeddings, making this the correct tool for the storage/querying task.
- D. Incorrect.
spaCy is not designed for querying word embeddings. Its primary focus is on natural language processing tasks like parsing and tokenization.
- E. Incorrect.
NumPy is not suitable for storing large-scale embeddings efficiently. It lacks the indexing and querying capabilities needed for this purpose.