Challenges in language processing in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to tokenize the sentence into words.

NLP

tokens = sentence.[1]()

Drag options to blanks, or click blank then click option'

Asplit

Bjoin

Creplace

Dstrip

Attempts:

3 left

💡 Hint

Common Mistakes

Using join() instead of split()

Using replace() which changes characters

The split() method breaks a sentence into words by spaces, which is basic tokenization.

2fill in blank

medium

Complete the code to convert all tokens to lowercase for uniformity.

NLP

tokens = [word.[1]() for word in tokens]

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Alower

Bcapitalize

Cupper

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using upper() which makes all letters uppercase

Using capitalize() which only changes first letter

The lower() method converts all characters in a string to lowercase, helping uniform text processing.

3fill in blank

hard

Fix the error in the code to remove punctuation from each token.

NLP

import string
clean_tokens = [word.strip(string.[1]) for word in tokens]

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Aascii_letters

Bwhitespace

Cpunctuation

Ddigits

Attempts:

3 left

💡 Hint

Common Mistakes

Using whitespace which removes spaces, not punctuation

Using digits which removes numbers

string.punctuation contains all punctuation characters to strip from words.

4fill in blank

hard

Fill both blanks to create a dictionary counting word frequencies.

NLP

word_freq = {word: tokens.[1](word) for word in tokens if word [2] ''}

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Acount

B!=

C==

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using == instead of != in condition

Using append which is for lists, not counting

count counts occurrences of each word. The condition word != '' skips empty strings.

5fill in blank

hard

Fill all three blanks to filter out stopwords and create a list of important words.

NLP

stopwords = {'and', 'the', 'is', 'in'}
important_words = [word for word in tokens if word [1] stopwords and len(word) [2] [3]]

Drag options to blanks, or click blank then click option'

Anot in

B>

C2

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'in' instead of 'not in' to exclude stopwords

Using '<' instead of '>' for length condition

We exclude stopwords with not in. We keep words longer than 2 characters using len(word) > 2.

Practice

1. Why is language processing challenging for computers?

easy

A. Because computers do not have enough memory

B. Because computers cannot store large amounts of data

C. Because language has only one fixed meaning per word

D. Because words can have multiple meanings depending on context

Solution

1. Step 1: Understand word ambiguity in language

   Words often have several meanings, which depend on the context they appear in.

2. Step 2: Relate ambiguity to computer difficulty

   Computers struggle to pick the correct meaning without understanding context, making language processing hard.

3. Final Answer:

   Because words can have multiple meanings depending on context -> Option D

4. Quick Check:

   Word ambiguity = D [OK]

Hint: Remember: words change meaning with context [OK]

Common Mistakes:

• Thinking each word has only one meaning
• Assuming computers lack memory causes difficulty
• Confusing data storage with language understanding

2. Which of the following is the correct way to represent a sentence tokenization step in Python using NLTK?

easy

A. tokens = nltk.word_tokenize(sentence)

B. tokens = nltk.sentence_tokenize(sentence)

C. tokens = nltk.tokenize_words(sentence)

D. tokens = nltk.split(sentence)

Solution

1. Step 1: Recall NLTK tokenization functions

   NLTK uses word_tokenize() to split sentences into words (tokens).

2. Step 2: Identify correct function for word tokenization

   word_tokenize() is the correct function; sentence_tokenize() does not exist, and others are invalid.

3. Final Answer:

   tokens = nltk.word_tokenize(sentence) -> Option A

4. Quick Check:

   NLTK word tokenization = C [OK]

Hint: Use word_tokenize() for splitting sentence into words [OK]

Common Mistakes:

• Using sentence_tokenize() which is not a valid function
• Confusing word_tokenize() with tokenize_words()
• Trying to split sentence with split() method

3. Given the code below, what will be the output?

sentence = "I saw her duck." 
tokens = sentence.split()
print(tokens)

medium

A. ['I', 'saw', 'her', 'duck.']

B. ['I', 'saw', 'her', 'duck']

C. ['I', 'saw', 'her', 'duck', '.']

D. ['I saw her duck']

Solution

1. Step 1: Understand split() behavior on string

   split() divides the string by spaces, keeping punctuation attached to words.

2. Step 2: Apply split() to the sentence

   Splitting "I saw her duck." by spaces results in ['I', 'saw', 'her', 'duck.'] with the period attached to 'duck.'

3. Final Answer:

   ['I', 'saw', 'her', 'duck.'] -> Option A

4. Quick Check:

   split() keeps punctuation attached = A [OK]

Hint: split() keeps punctuation with words [OK]

Common Mistakes:

• Assuming split() removes punctuation
• Expecting punctuation as separate token
• Confusing split() with word_tokenize()

4. The following code tries to remove stopwords from a list of tokens but does not work as expected. What is the error?

stopwords = ['the', 'is', 'at']
tokens = ['the', 'cat', 'is', 'on', 'the', 'mat']
filtered = [word for word in tokens if word not in stopwords()]
print(filtered)

medium

A. tokens should be converted to a set before filtering

B. The list comprehension syntax is incorrect

C. stopwords is a list, not a function; should not use parentheses

D. The print statement is missing parentheses

Solution

1. Step 1: Identify the error in stopwords usage

   stopwords is a list, but the code uses stopwords() as if it were a function.

2. Step 2: Correct the usage of stopwords

   Remove parentheses to use stopwords as a list: use 'word not in stopwords' instead of 'stopwords()'.

3. Final Answer:

   stopwords is a list, not a function; should not use parentheses -> Option C

4. Quick Check:

   stopwords list misuse = B [OK]

Hint: Lists are not functions; avoid parentheses [OK]

Common Mistakes:

• Using parentheses after list variable
• Thinking tokens must be sets to filter
• Misreading list comprehension syntax

5. Which challenge best explains why idioms like "kick the bucket" are hard for AI to understand?

hard

A. Idioms are always spelled incorrectly

B. Idioms have meanings different from the literal words

C. Idioms contain rare words not in dictionaries

D. Idioms are too long for AI to process

Solution

1. Step 1: Understand idioms in language

   Idioms are phrases whose meaning is not the sum of their individual words.

2. Step 2: Relate idioms to AI language challenges

   AI struggles because it cannot infer the non-literal meaning from the literal words alone.

3. Final Answer:

   Idioms have meanings different from the literal words -> Option B

4. Quick Check:

   Idioms = non-literal meaning = A [OK]

Hint: Idioms mean more than their words [OK]

Common Mistakes:

• Thinking idioms are misspelled
• Assuming idioms use rare words
• Believing idioms are too long to process