Why N-gram language models in NLP? - Purpose & Use Cases

The Big Idea

What if your phone could finish your sentences just like a friend does?

The Scenario

Imagine trying to predict the next word in a sentence by remembering every possible word combination you have ever seen. For example, guessing what comes after "I love to" by recalling all sentences you read before.

The Problem

Doing this by hand is slow and confusing because there are so many word combinations. It's easy to forget some or make wrong guesses, and it takes forever to check all possibilities.

The Solution

N-gram language models break down sentences into small groups of words and count how often they appear together. This helps computers quickly guess the next word based on recent words, making predictions smarter and faster.

Before vs After

✗ Before

if last_words == ['I', 'love', 'to']:
    guess = 'eat'  # hardcoded guess

✓ After

guess = ngram_model.predict_next(['I', 'love', 'to'])

What It Enables

It lets computers understand and predict language patterns, powering things like text suggestions, speech recognition, and chatbots.

Real Life Example

When you type on your phone and it suggests the next word, it uses models like N-grams to guess what you might want to say next.

Key Takeaways

Manual word prediction is slow and error-prone.

N-gram models use word groups to predict next words efficiently.

This helps computers understand and generate human-like text.

Practice

1. What does an n-gram language model primarily do?

easy

A. Predict the next word based on previous words

B. Translate text from one language to another

C. Generate images from text descriptions

D. Detect the sentiment of a sentence

Solution

1. Step 1: Understand the purpose of n-gram models

   N-gram models look at sequences of words to predict what comes next.

2. Step 2: Identify the main function

   They use previous words to guess the next word in a sentence.

3. Final Answer:

   Predict the next word based on previous words -> Option A

4. Quick Check:

   N-gram models predict next word = A [OK]

Hint: N-grams predict next word from previous words [OK]

Common Mistakes:

• Confusing n-gram with translation models
• Thinking n-grams generate images
• Mixing up sentiment analysis with n-grams

2. Which of the following is the correct way to represent a bigram from the sentence 'I love AI'?

easy

A. ('AI', 'love')

B. ('I', 'love')

C. ('love', 'AI', 'I')

D. ('I', 'AI')

Solution

1. Step 1: Understand bigrams

   Bigrams are pairs of consecutive words in a sentence.

2. Step 2: Extract bigrams from 'I love AI'

   The pairs are ('I', 'love') and ('love', 'AI'). ('I', 'love') shows a correct bigram.

3. Final Answer:

   ('I', 'love') -> Option B

4. Quick Check:

   Bigram = consecutive word pairs = C [OK]

Hint: Bigrams are pairs of consecutive words [OK]

Common Mistakes:

• Including three words instead of two
• Mixing word order in pairs
• Selecting non-consecutive words

3. Given the sentence 'the cat sat on the mat', what is the count of the trigram ('the', 'cat', 'sat')?

medium

A. 0

B. 2

C. 1

D. 3

Solution

1. Step 1: Identify trigrams in the sentence

   Trigrams are sequences of three consecutive words. The trigrams are: ('the', 'cat', 'sat'), ('cat', 'sat', 'on'), ('sat', 'on', 'the'), ('on', 'the', 'mat').

2. Step 2: Count the trigram ('the', 'cat', 'sat')

   This trigram appears once at the start of the sentence.

3. Final Answer:

   1 -> Option C

4. Quick Check:

   Trigram count = 1 [OK]

Hint: Count exact three-word sequences in order [OK]

Common Mistakes:

• Counting non-consecutive words
• Confusing bigrams with trigrams
• Overcounting repeated words

4. Consider this Python code snippet to generate bigrams from a list of words:

words = ['hello', 'world', 'hello']
bigrams = [(words[i], words[i+1]) for i in range(len(words))]

What error will this code produce?

medium

A. No error, code runs correctly

B. SyntaxError: invalid syntax

C. TypeError: unsupported operand type(s)

D. IndexError: list index out of range

Solution

1. Step 1: Analyze the loop range

   The loop runs from 0 to len(words)-1, which is 0 to 2 for 3 words.

2. Step 2: Check index access inside loop

   At i=2, words[i+1] tries to access words[3], which is out of range, causing IndexError.

3. Final Answer:

   IndexError: list index out of range -> Option D

4. Quick Check:

   Loop index exceeds list length = D [OK]

Hint: Check loop range when accessing i+1 index [OK]

Common Mistakes:

• Using full length in range causing out-of-bounds
• Assuming no error without testing
• Confusing syntax errors with runtime errors

5. You want to build a trigram model from a text corpus but notice many rare trigrams cause sparse data issues. Which technique can help improve your model's predictions?

hard

A. Use smoothing methods like Laplace smoothing

B. Increase the n in n-gram to 5-grams

C. Remove all trigrams that appear less than 10 times

D. Ignore the problem and use raw counts

Solution

1. Step 1: Understand sparse data in n-gram models

   Rare trigrams cause zero or low counts, making predictions unreliable.

2. Step 2: Identify smoothing techniques

   Smoothing like Laplace adds small counts to all n-grams, reducing zero probabilities and improving predictions.

3. Final Answer:

   Use smoothing methods like Laplace smoothing -> Option A

4. Quick Check:

   Smoothing reduces sparse data issues = A [OK]

Hint: Apply smoothing to handle rare n-grams [OK]

Common Mistakes:

• Increasing n worsens sparsity
• Removing rare n-grams loses useful info
• Ignoring sparsity leads to poor predictions