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Natural language processing can be understood as a subarea of artificial intelligence that studies the automatic understanding of natural languages. In this context, it can be stated that the following tasks are used in natural language processing problems:
Real Exam
Community
DDanyel
A
Stopwords and semantic segmentation.
B
Quantization and Part-of-Speech.
C
Entity extraction and tokenization.
D
Bigrams and recurrent memories.
E
Spectrograms and enhancement.
Explanation:
Explanation
Correct Answer: C (Entity extraction and tokenization)
Why C is correct:
- Entity extraction (also called named entity recognition) is a fundamental NLP task that identifies and classifies key information in text such as names of people, organizations, locations, dates, etc.
- Tokenization is the process of breaking text into smaller units (tokens) such as words, subwords, or characters, which is the first step in most NLP pipelines.
Why other options are incorrect:
A) Stopwords and semantic segmentation:
- Stopwords removal is indeed an NLP preprocessing step (removing common words like "the", "is", "and"), but semantic segmentation is more related to computer vision (segmenting images based on semantic meaning) rather than NLP.
B) Quantization and Part-of-Speech:
- Part-of-Speech (POS) tagging is a core NLP task that labels words with their grammatical categories (noun, verb, adjective, etc.).
- Quantization is primarily a machine learning optimization technique for reducing model size, not a specific NLP task.
D) Bigrams and recurrent memories:
- Bigrams (pairs of consecutive words) are used in NLP for language modeling and feature extraction.
- "Recurrent memories" is not a standard NLP term; it might refer to recurrent neural networks (RNNs) which are used in NLP, but the pairing is less precise.
E) Spectrograms and enhancement:
- Spectrograms are used in speech processing (audio signal processing), which is related to but distinct from NLP. Enhancement techniques are more associated with signal processing than core NLP tasks.
Key NLP Tasks:
- Tokenization - Breaking text into tokens
- Entity Extraction/NER - Identifying named entities
- Part-of-Speech Tagging - Grammatical classification
- Parsing - Analyzing sentence structure
- Sentiment Analysis - Determining emotional tone
- Machine Translation - Translating between languages
- Text Classification - Categorizing text documents
While some elements from other options are related to NLP (like stopwords, POS tagging, bigrams), only option C contains two tasks that are both fundamental and specific to NLP processing pipelines.
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