Discover how text normalization in NLP simplifies data preprocessing by removing punctuation, converting text to lowercase, and more. Learn its role in improving AI efficiency.
Table of Contents
Question
Which of the following concepts related to Natural Language Processing (NLP) workloads involves removing punctuation and changing words to lowercase?
A. Stemming
B. N-grams
C. Text normalization
D. Sentiment analysis
Answer
C. Text normalization
Explanation
Text normalization in Natural Language Processing (NLP) involves standardizing and cleaning text data. This includes removing punctuation and converting words to lowercase to ensure consistent analysis and processing. Text analytics involves analyzing and processing natural language to derive meaningful insights. Key concepts related to NLP are:
Tokenization:
- Breaks text into tokens (words or partial words).
- Considerations: Text normalization, stop-word removal, n-grams, and stemming.
Frequency Analysis:
- Counts occurrences of each token.
- Importance: Identifies the most common words, aiding in understanding the document’s subject.
Term Frequency-Inverse Document Frequency (TF-IDF):
- Measures word relevance in a document relative to its frequency across all documents.
- Useful for analyzing multiple documents in a collection.
Machine Learning for Text Classification:
- Uses classification algorithms (e.g., logistic regression) to classify text.
- Applied in sentiment analysis, categorizing text as positive or negative.
Semantic Language Models:
- Embeds language tokens as vectors to capture semantic relationships.
- Tokens closer in space are more semantically related.
- Industry models are more complex, with higher-dimensional embeddings.
Common NLP Tasks:
- Text analysis, sentiment analysis, machine translation, summarization, and conversational AI.
- Supported by Azure AI Language service.
Stemming is a concept that involves reducing words to their base or root form. It does not specifically address punctuation or case normalization.
N-grams represent contiguous sequences of n items from a given sample of text or speech. It does not involve removing punctuation or changing the case of words.
Sentiment analysis is a process that categorizes text as positive, negative, or neutral based on the emotions expressed. It does not involve the removal of punctuation or changing words to their lower case which are characteristics of text normalization.
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