NLP_B1_W8

Which of the following is not typically classified as a named entity in NER tasks?

• Person
  

• Verb

• Date

• Location

What is Named Entity Recognition (NER)

• Identifying the parts of speech in a sentence
  

• Generating text from structured data

• Recognizing and classifying entities in a text such as names, locations, and dates

• Translating a sentence into another language

Which of the following algorithms is commonly used for Named Entity Recognition

• K-Nearest Neighbors (KNN)

• K-Means Clustering

• Conditional Random Fields (CRF)

• Decision Trees

In the context of NER, what does the acronym “IOB” stand for

• • Indexed-Organized-Beginning

• • Inside-Outside-Beginning

• • Input-Output-Beginning

• • Inside-Out-Beginning

Which of the following pre-trained transformer models is widely used for Named Entity Recognition

• • AlexNet

• • VGGNet

• • ResNet

• • BERT

Which method is used to break down a sentence into its grammatical components

• Stemming

• Tokenization

• Lemmatization

• Chunking

Which method is used to assign a sentiment label to a given text

• Dependency parsing

• Part-of-speech tagging

• Sentiment analysis

• Named entity recognition

What is the main input required to perform chunking in NLP

• • NER Tags

• • Dependency Trees

• • POS-Tagged Tokens

• • Raw Text

Which of the following types of chunks can be identified by chunking

• • Sentiment Scores (SS)

• • Named Entities (NE) and Dates (D)

• • Sentences (S) and Clauses (C)

• • Noun Phrases (NP) and Verb Phrases (VP)

What is the primary difference between chunking and parsing

• • Chunking operates on tokens, while parsing operates on raw text

• • Parsing is done before chunking

• • Chunking groups words into phrases, while parsing generates a full syntactic structure of a sentence

• • Parsing groups words into entities, while chunking assigns parts of speech