Why Handling out-of-vocabulary words in NLP? - Purpose & Use Cases

The Big Idea

What if your computer could understand words it has never seen before, just like you do?

The Scenario

Imagine you are teaching a computer to understand text messages, but it only knows a fixed list of words. When someone uses a new slang or a typo, the computer gets confused and can't understand the message.

The Problem

Manually updating the computer's word list every time a new word appears is slow and tiring. It's easy to miss words, and the computer keeps failing to understand new or rare words, making it less helpful.

The Solution

Handling out-of-vocabulary words means teaching the computer smart ways to guess or break down unknown words automatically. This way, it can still understand or make sense of new words without needing constant updates.

Before vs After

✗ Before

if word in vocabulary:
    use_word(word)
else:
    ignore_word()

✓ After

if word in vocabulary:
    use_word(word)
else:
    use_subword_or_guess(word)

What It Enables

This lets computers understand new words and slang instantly, making language tools smarter and more flexible.

Real Life Example

When you type a new slang word in your phone's keyboard, it still suggests corrections or understands your message because it can handle words it never saw before.

Key Takeaways

Manual word lists can't keep up with new words.

Handling out-of-vocabulary words helps computers guess or break down unknown words.

This makes language tools smarter and more user-friendly.

Practice

1. What is the main purpose of using an <UNK> token in natural language processing?

easy

A. To separate words in a sentence

B. To mark the end of a sentence

C. To represent words not seen during training

D. To highlight important keywords

Solution

1. Step 1: Understand the role of <UNK> token

   The <UNK> token is used to replace words that the model has not seen during training, known as out-of-vocabulary words.

2. Step 2: Identify the correct purpose

   Since <UNK> stands for unknown words, it helps the model handle new or rare words by treating them as a single token.

3. Final Answer:

   To represent words not seen during training -> Option C

4. Quick Check:

   <UNK> = unknown words [OK]

Hint: Think of <UNK> as a placeholder for unknown words [OK]

Common Mistakes:

• Confusing <UNK> with sentence delimiters
• Using <UNK> for common words
• Thinking <UNK> highlights keywords

2. Which of the following is the correct way to replace out-of-vocabulary words with <UNK> in a Python list of tokens named tokens given a vocabulary set vocab?

easy

A. tokens = [word if word in vocab else '<UNK>' for word in tokens]

B. tokens = [word for word in tokens if word in vocab else '<UNK>']

C. tokens = [word in vocab ? word : '<UNK>' for word in tokens]

D. tokens = [word if word not in vocab else '<UNK>' for word in tokens]

Solution

1. Step 1: Understand list comprehension syntax

   The correct Python syntax for conditional expressions inside a list comprehension is: [x if condition else y for x in list].

2. Step 2: Apply correct condition for replacing OOV words

   We want to keep the word if it is in the vocabulary; otherwise, replace it with '<UNK>'. tokens = [word if word in vocab else '<UNK>' for word in tokens] correctly implements this logic.

3. Final Answer:

   tokens = [word if word in vocab else '<UNK>' for word in tokens] -> Option A

4. Quick Check:

   Correct Python conditional list comprehension [OK]

Hint: Remember: x if condition else y inside list comprehensions [OK]

Common Mistakes:

• Using incorrect syntax like 'if-else' outside list comprehension
• Confusing Python with other languages' ternary syntax
• Reversing the condition logic

3. Given the following code snippet, what will be the output?

vocab = {'hello', 'world'}
tokens = ['hello', 'there', 'world']
tokens = [word if word in vocab else '<UNK>' for word in tokens]
print(tokens)

medium

A. ['hello', 'there', 'world']

B. ['hello', 'world', '<UNK>']

C. ['<UNK>', '<UNK>', '<UNK>']

D. ['hello', '<UNK>', 'world']

Solution

1. Step 1: Check each token against the vocabulary

   'hello' is in vocab, so it stays 'hello'. 'there' is not in vocab, so it becomes '<UNK>'. 'world' is in vocab, so it stays 'world'.

2. Step 2: Construct the resulting list

   The new tokens list is ['hello', '<UNK>', 'world'].

3. Final Answer:

   ['hello', '<UNK>', 'world'] -> Option D

4. Quick Check:

   Replace OOV words with <UNK> [OK]

Hint: Replace words not in vocab with <UNK> [OK]

Common Mistakes:

• Not replacing 'there' because of misunderstanding
• Replacing all words regardless of vocab
• Confusing list order in output

4. The following code is intended to replace out-of-vocabulary words with <UNK>. What is the error?

vocab = {'cat', 'dog'}
tokens = ['cat', 'bird', 'dog']
tokens = [word if word not in vocab else '<UNK>' for word in tokens]
print(tokens)

medium

A. The vocabulary should be a list, not a set

B. The condition is reversed; it replaces in-vocab words instead of OOV

C. The list comprehension syntax is invalid

D. The print statement is missing parentheses

Solution

1. Step 1: Analyze the condition in list comprehension

   The condition word if word not in vocab else '<UNK>' means words NOT in vocab stay as they are, and words IN vocab become '<UNK>'. This is the opposite of the intended behavior.

2. Step 2: Identify the correct logic

   We want to keep words in vocab and replace words not in vocab with '<UNK>'. So the condition should be word if word in vocab else '<UNK>'.

3. Final Answer:

   The condition is reversed; it replaces in-vocab words instead of OOV -> Option B

4. Quick Check:

   Correct condition keeps vocab words, replaces others [OK]

Hint: Check if condition matches intended keep-or-replace logic [OK]

Common Mistakes:

• Mixing up 'in' and 'not in' in conditions
• Assuming set vs list affects membership test
• Ignoring Python 3 print syntax

5. You have a pretrained word embedding model that does not include the word 'unicorn'. Which approach best helps your model handle this out-of-vocabulary word during inference?

hard

A. Use subword tokenization to break 'unicorn' into known parts

B. Ignore 'unicorn' and remove it from the input

C. Add 'unicorn' to the vocabulary without retraining

D. Replace 'unicorn' with <UNK> token embedding

Solution

1. Step 1: Understand limitations of <UNK> token

   Replacing with <UNK> loses specific meaning, which may reduce model accuracy.

2. Step 2: Consider subword tokenization benefits

   Subword tokenization breaks unknown words into smaller known units, allowing the model to infer meaning from parts.

3. Step 3: Evaluate other options

   Ignoring the word loses information; adding it without retraining is not feasible; subword tokenization is the best practical approach.

4. Final Answer:

   Use subword tokenization to break 'unicorn' into known parts -> Option A

5. Quick Check:

   Subword methods handle OOV words better than <UNK> [OK]

Hint: Break unknown words into smaller known pieces with subword tokenization [OK]

Common Mistakes:

• Thinking <UNK> always preserves meaning
• Trying to add words without retraining embeddings
• Removing unknown words loses important info