What are n-grams, and how are they used in NLP?

Explanation:

N-grams are contiguous sequences of 'n' items from a given text or speech. In NLP, these items can be characters, syllables, or words. N-grams are used to analyze the structure of a language by capturing the context or pattern of these items. They help in various tasks like text prediction, classification, and language modeling by providing a statistical model for the likelihood of a sequence.

Key Talking Points:

• Definition: N-grams are sequences of 'n' consecutive items from a text.
• Usage in NLP: Used for language modeling, text prediction, and classification tasks.
• Types: Unigrams (n=1), Bigrams (n=2), Trigrams (n=3), etc.
• Benefits: Capture contextual information and dependencies in text.
• Limitations: Can become computationally expensive with large 'n' and may ignore long-range dependencies.

NOTES:

Reference Table:

Pseudocode:

   def generate_ngrams(text, n):
       words = text.split()
       ngrams = []
       for i in range(len(words) - n + 1):
           ngram = ' '.join(words[i:i + n])
           ngrams.append(ngram)
       return ngrams

   # Example usage
   text = "I love NLP"
   bigrams = generate_ngrams(text, 2)
   print(bigrams)  # Output: ['I love', 'love NLP']

Follow-Up Questions and Answers:

1. Question: How do n-grams deal with the problem of data sparsity?

   • Answer: N-grams can lead to data sparsity because the number of possible n-grams increases exponentially with 'n'. This can be mitigated using smoothing techniques like Laplace smoothing, which assigns a small probability to unseen n-grams.

2. Question: Can you explain how n-grams are used in text classification?

   • Answer: In text classification, n-grams serve as features to represent documents. By converting text into n-grams, we create a feature set that captures contextual information. These features can then be used with machine learning algorithms to categorize text into different classes.

3. Question: What are some limitations of using n-grams in NLP?

   • Answer: N-grams can ignore long-range dependencies due to their fixed size, become computationally expensive with large text corpora, and may not capture semantic meaning effectively. Additionally, they can result in high-dimensional data, leading to the curse of dimensionality.