Unicode handling in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Unicode string length

What is the output of this Python code snippet?

NLP

text = 'café'
print(len(text))

A SyntaxError

B 5

C 3

D 4

💡 Hint

Count the number of characters in the string, not bytes.

The string 'café' has 4 characters: c, a, f, and é. The length counts characters, not bytes.

🧠 Conceptual

intermediate

Why normalize Unicode text?

Why is Unicode normalization important in text processing for machine learning?

A To ensure visually identical characters have the same binary representation

B To convert all text to uppercase for consistency

C To remove all accents and special characters

D To translate text into English before processing

💡 Hint

Think about how the same character can be represented differently in Unicode.

Normalization makes sure that characters that look the same are stored the same way, avoiding mismatches.

❓ Metrics

advanced

Effect of Unicode normalization on token counts

Given a text with accented characters, how does Unicode normalization affect token counts in NLP preprocessing?

A Normalization always increases the number of tokens

B Normalization can reduce token count by merging equivalent characters

C Normalization has no effect on token counts

D Normalization splits tokens into multiple parts

💡 Hint

Consider how different Unicode forms might split or merge characters.

Normalization merges different Unicode representations of the same character, which can reduce token variations and counts.

🔧 Debug

advanced

Identify the error in Unicode decoding

What error will this Python code raise when decoding bytes?

NLP

data = b'caf\xe9'
text = data.decode('utf-8')

A UnicodeDecodeError

B SyntaxError

C TypeError

D No error, output is 'café'

💡 Hint

Check if the byte sequence is valid UTF-8.

The byte sequence b'caf\xe9' is invalid UTF-8 because \xe9 alone is not a valid UTF-8 character.

❓ Model Choice

expert

Choosing model input for multilingual text with Unicode

You want to train a machine learning model on multilingual text containing many Unicode characters. Which input representation is best to handle Unicode properly?

A Use UTF-8 encoded byte sequences directly as input

B Use ASCII encoding and ignore non-ASCII characters

C Use Unicode code points or character embeddings after normalization

D Convert all text to lowercase ASCII equivalents

💡 Hint

Think about preserving all characters and their meanings for the model.

Using Unicode code points or embeddings after normalization preserves all characters and their semantic meaning for the model.