Podcast
Questions and Answers
What is the main purpose of Name Entity Recognition (NER)?
What is the main purpose of Name Entity Recognition (NER)?
- Identifying verbs and adjectives
- Identifying specific entities in a text document (correct)
- Identifying punctuation marks
- Identifying proper nouns only
How does Masked Language Modelling help learners?
How does Masked Language Modelling help learners?
- By mastering deep representations in downstream tasks (correct)
- By focusing only on corrupted input
- By providing direct answers to questions
- By avoiding the use of language models
What does pragmatic analysis in NLP primarily focus on?
What does pragmatic analysis in NLP primarily focus on?
- Syntax and grammar
- Outside world knowledge (correct)
- Internal document structure
- Historical language usage
In NLP, what does perplexity refer to?
In NLP, what does perplexity refer to?
What is the primary function of entity chunking in Name Entity Recognition?
What is the primary function of entity chunking in Name Entity Recognition?
How does Pragmatic Analysis contribute to understanding language?
How does Pragmatic Analysis contribute to understanding language?
In the context of NLP, what role does Perplexity play in language models?
In the context of NLP, what role does Perplexity play in language models?
What aspect of language does Pragmatic Analysis focus on?
What aspect of language does Pragmatic Analysis focus on?
"Chunking" entities in NER involves:
"Chunking" entities in NER involves:
Study Notes
Perplexity in NLP
- Perplexity measures the uncertainty in predicting text in NLP
- It is a way to evaluate language models
- Low perplexity is desirable, indicating less difficulty in handling complicated problems
- High perplexity is undesirable, indicating a high failure rate in handling complicated problems
N-gram in NLP
- An n-gram is a sequence of n words
- It helps identify sentences that appear more frequently
- Assigning probability to n-gram occurrences can aid in next-word predictions and spelling error corrections
Differences between AI, Machine Learning, and NLP
- Not provided in the given text (question without answer)
Self-Attention
- Self-attention is not a supervised learning technique
- It is a powerful tool in NLP
LDA (Latent Dirichlet Allocation)
- LDA is an unsupervised learning model
- It is used for topic modeling
- The selection of the number of topics in LDA depends on the size of the data
- The number of topic terms is not directly proportional to the size of the data
Hyperparameters in LDA
- Alpha (α) represents the density of topics generated within documents
- Beta (β) represents the density of terms generated within topics
Issues with ReLu
- Exploding gradient: solved by gradient clipping
- Dying ReLu: solved by parametric ReLu
- Mean and variance of activations are not 0 and 1: partially solved by subtracting around 0.5 from activation
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
This quiz covers the concept of perplexity in NLP, which is a measure of uncertainty in predicting text, and explains the significance of high and low perplexity. It also explores the definition of n-gram in NLP as a sequence of n words commonly used in language modeling.
