NCA-GENL Question 183
Select 3You are tasked with performing Named Entity Recognition (NER) on a dataset of customer reviews using spaCy. After extracting the entities, you want to perform numerical analysis on the frequency of each entity type and visualize the results. Which combination of Python packages would be most appropriate for this task?
- A
spaCy for extracting entities and NumPy for numerical analysis
- B
spaCy for extracting entities and Matplotlib for visualization
- C
Keras for entity extraction and NumPy for numerical analysis
- D
NumPy for entity extraction and Matplotlib for visualization
- E
spaCy for extracting entities, NumPy for numerical analysis, and Matplotlib for visualization
Show answer and explanation
Correct answers: A, B, E
Explanation
To perform Named Entity Recognition (NER) and analyze the frequency of entities, spaCy is an appropriate choice for entity extraction. NumPy is well-suited for numerical tasks such as calculating frequencies, and Matplotlib is a powerful library for creating visualizations. Using these tools together (options 1, 2, and 5) ensures the task is performed effectively.
- A. Correct.
Correct: spaCy is highly suitable for Named Entity Recognition (NER), and NumPy can be used to perform numerical analysis such as finding entity frequencies.
- B. Correct.
Correct: spaCy can extract entities, and Matplotlib is commonly used for visualizing data, such as creating bar charts for entity frequencies.
- C. Incorrect.
Incorrect: Keras is primarily used for building and training deep learning models, not for NER or numerical analysis.
- D. Incorrect.
Incorrect: NumPy does not support entity extraction, and Matplotlib alone cannot handle the task described. This combination is insufficient.
- E. Correct.
Correct: Using spaCy for NER, NumPy for frequency analysis, and Matplotlib for visualization combines the strengths of all three packages to complete the task efficiently.