16 Questions
What is the purpose of tokenization in natural language processing?
Splitting the sentence into a set of tokens/words based on spaces
What is a challenge in tokenization in natural language processing?
Lack of spaces between words in languages like Japanese and Chinese
What is the purpose of sentence segmentation in natural language processing?
Dividing the context into a set of sentences using ML algorithms
What does POS tagging aim to determine in natural language processing?
Lexical category of the word based on its meaning in the context
Which step of the traditional NLP pipeline involves identifying Named Entities within the text?
NER (Named Entity Recognition)
What is one of the purposes of using ML algorithms for sentence segmentation?
Finding End Of Statements (EOS)
What is the main focus of POS tagging?
Identifying and categorizing parts of speech in a sentence
Which type of word reduction removes all affixes to reduce the word to its stem?
Stemming
What is the purpose of Name Entity Recognition (NER)?
Identifying and classifying important names in text
What is the role of Lemmatization in word processing?
Reducing words to their roots by removing all affixes
What technique aids in word disambiguation by reducing words to their roots?
Stemming
Which factor is considered in POS tagging for word disambiguation?
Knowledge of neighboring words in the context
Which technology is NOT supported by Spacy for word processing?
Stemming
What is the percentage accuracy limit of current models for POS tagging?
~97%
Which part of speech is NOT classified under Closed POS Classes?
Proper Noun
What is the focus of Closed vs. Open POS Classes?
Classifying different types of nouns and adjectives
Test your knowledge of Natural Language Processing (NLP) pipeline and tools with this quiz. Explore topics including NLP libraries like NLTK, Spacy, Gensim, Fasttext, and Pandas, as well as techniques such as tokenization, POS tagging, stemming, NER, and more.
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