Quiz on POS Tagging Techniques

What does Rule-Based POS Tagging primarily rely on?

• Statistical models

• A set of predefined rules

• Machine learning algorithms

• Neural networks

Which of the following is an example of a lexical rule?

• Words following a determiner are likely nouns

• Words ending in '-ly' are often adverbs

• Nouns are often preceded by adjectives

• Verbs typically follow subjects

What is a disadvantage of Rule-Based POS Tagging?

• Can be highly accurate in specific domains

• Labor-intensive to create and update rules

• Requires a large annotated corpus

• Adapts easily to new languages

What is the first step in Transformation-Based Learning (TBL)?

• Identify errors

• Create transformations

• Initial tagging

• Apply transformations

What does TBL stand for?

• Tagging Based Learning

• Transformation-Based Learning

• Text-Based Learning

• Token-Based Learning

Which algorithm is commonly used in HMM for decoding?

• K-means algorithm

• Gradient descent

• Viterbi algorithm

• Support vector machine

What are the 'states' in a Hidden Markov Model?

• The initial probabilities

• The words in the sentence

• The POS tags

• The transition probabilities

What is a key advantage of using HMM for POS tagging?

• No need for annotated data

• Simplicity in implementation

• Statistical robustness

• Requires no training data

What is a disadvantage of using Transformation-Based Learning?

• Limited adaptability

• High precision

• Requires a labeled corpus

• Low accuracy

What do emission probabilities represent in HMM?

• The probability of a word being a noun

• The probability of a tag being the starting tag

• The probability of a word given a tag

• The probability of transitioning from one tag to another