11 Questions
Explain the purpose of Hidden Markov Model (HMM) in natural language processing (NLP).
HMM is used in NLP to capture dependencies between words by determining the transition and emission probabilities of different parts of speech and word meanings.
What are the differences between Named Entity Recognition (NER) and Chunking in NLP?
NER is the identification and classification of named entities such as person, organization, place, date, and time, while chunking involves dividing sentences into syntactically meaningful parts.
What are the types of named entities that are typically recognized in Named Entity Recognition (NER)?
The typical types of named entities recognized in NER are person, organization, place, date, and time.
How do models like Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), and Transformer capture semantic nuances in natural language processing (NLP)?
These models capture complex semantic nuances by analyzing the contextual relationships between words and understanding the meaning and usage of words in different contexts.
In the context of natural language processing (NLP), how is chunking used in information extraction?
Chunking is used to divide sentences into syntactically meaningful parts, which aids in information extraction for tasks such as search algorithms, recommendation systems, and customer feedback analysis.
Provides insights into syntactic structure, disambiguates word meanings and determines relationships
Statistical Models like Hidden Markov Model (HMM) capture dependencies between words HMM consists of multiplying all Transition and Emission Probability Emission: Probability of Tag (Noun) being given word (Mary) Transition: Probability of transition from one tag to another NN : Models like Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM) and Transformer Models capture complex semantic nuances Named Entity Recognition (NER) : Identification and classification of named entities Types : Person, Organization, Place, Date and Time Input: Tokens with respective POS tag Output: Tokens with POS tag + named entities are grouped and given a type Used in info extraction: Search algorithm, recommendation system, customer feedback. Chunking : Dividing sentences int.
"Models like Hidden Markov Model (HMM) capture dependencies between ______"
words
"Transition: Probability of transition from one tag to another ______"
tag
"Named Entity Recognition (NER) : Identification and classification of named ______"
entities
"Types : Person, Organization, Place, Date and ______"
Time
"Chunking : Dividing sentences ______"
int
Test your knowledge of text preprocessing and tokenization with this quiz. Explore concepts such as breaking down text into tokens, handling out of vocabulary words, and the challenges of word tokenization.
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