Handling out-of-vocabulary words in NLP - Model Pipeline Trace

Model Pipeline - Handling out-of-vocabulary words

This pipeline shows how a text model handles words it has never seen before, called out-of-vocabulary (OOV) words. It transforms text into numbers, trains a simple model, and predicts while managing unknown words gracefully.

Data Flow - 6 Stages

1Raw Text Input

5 sentences x variable length→Collect raw sentences with some unknown words→5 sentences x variable length

"I love apples", "She eats bananas", "They like grapes", "We enjoy mangoes", "He hates durians"

↓

2Tokenization

5 sentences x variable length→Split sentences into words→5 sentences x variable length tokens

[['I', 'love', 'apples'], ['She', 'eats', 'bananas'], ['They', 'like', 'grapes'], ['We', 'enjoy', 'mangoes'], ['He', 'hates', 'durians']]

↓

3Vocabulary Building

All tokens from training sentences→Create a word-to-index map with a fixed vocabulary size and reserve an index for OOV→Vocabulary size = 7 (6 known words + 1 OOV token)

{"I":1, "love":2, "apples":3, "She":4, "eats":5, "bananas":6, "<OOV>":0}

↓

4Text to Sequence Conversion

5 sentences x tokens→Replace words with their indices; unknown words get OOV index 0→5 sentences x token indices

[[1, 2, 3], [4, 5, 6], [0, 0, 0], [0, 0, 0], [0, 0, 0]]

↓

5Padding Sequences

5 sentences x variable length→Pad sequences to max length 3 with zeros→5 sentences x 3 tokens

[[1, 2, 3], [4, 5, 6], [0, 0, 0], [0, 0, 0], [0, 0, 0]]

↓

6Model Training

5 samples x 3 tokens→Train a simple neural network to classify sentences→Model weights updated

Training on sequences with OOV tokens handled as index 0

Training Trace - Epoch by Epoch

Epochs
1 |***************         | Loss 0.85
2 |********************    | Loss 0.65
3 |************************| Loss 0.45

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.40	Model starts learning, loss high, accuracy low
2	0.65	0.60	Loss decreases, accuracy improves as model learns
3	0.45	0.80	Model converges, good accuracy on training data

Prediction Trace - 4 Layers

Layer 1: Input Sentence

Layer 2: Text to Sequence

Layer 3: Padding

Layer 4: Model Prediction

Model Quiz - 3 Questions

What happens to words not in the vocabulary during text to sequence conversion?

A They are assigned random indices

B They are replaced with a special OOV token index

C They are removed from the sentence

D They cause an error and stop processing

Key Insight

Handling out-of-vocabulary words by mapping them to a special token index allows the model to process unknown words without errors. This approach keeps the input consistent and helps the model generalize better to new text.

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