Amazon
MLS-C01 · Question #32
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low…
The correct answer is B. Amazon SageMaker BlazingText cbow mode. Blazing text has out-of-vocabulary (OOV) feature which can embed the non vocabulary words. https://docs.aws.amazon.com/sagemaker/latest/dg/blazingtext.html
Modeling
Question
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset. Which tool should be used to improve the validation accuracy?
Options
- AAmazon Comprehend syntax analysis and entity detection
- BAmazon SageMaker BlazingText cbow mode
- CNatural Language Toolkit (NLTK) stemming and stop word removal
- DScikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer
How the community answered
(17 responses)- A12% (2)
- B82% (14)
- D6% (1)
Explanation
Blazing text has out-of-vocabulary (OOV) feature which can embed the non vocabulary words. https://docs.aws.amazon.com/sagemaker/latest/dg/blazingtext.html
Topics
#Natural Language Processing (NLP)#Word Embeddings#Amazon SageMaker#BlazingText
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