Part-of-speech tagging in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the NLTK library for part-of-speech tagging.

NLP

import nltk

text = "I love learning AI"
tokens = nltk.word_tokenize(text)
pos_tags = nltk.[1](tokens)
print(pos_tags)

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

AposTag

Btag_pos

Ctagger

Dpos_tag

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong function name like tag_pos or posTag.

Forgetting to tokenize the text before tagging.

The correct function to get part-of-speech tags in NLTK is pos_tag().

2fill in blank

medium

Complete the code to tokenize the sentence before tagging.

NLP

sentence = "She enjoys reading books"
tokens = [1](sentence)
print(tokens)

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

Asentence.split

Bsentence.tokenize

Cnltk.word_tokenize

Dnltk.tokenize_words

Attempts:

3 left

💡 Hint

Common Mistakes

Using sentence.split which does not handle punctuation well.

Using a non-existent function like sentence.tokenize.

The nltk.word_tokenize() function splits the sentence into words (tokens).

3fill in blank

hard

Fix the error in the code to correctly tag the tokens.

NLP

tokens = ['They', 'are', 'playing', 'football']
pos_tags = nltk.pos_tag([1])
print(pos_tags)

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

Atokens()

Btokens

Ctokens[]

Dtoken

Attempts:

3 left

💡 Hint

Common Mistakes

Calling tokens as a function with parentheses.

Using incorrect syntax like tokens[].

The pos_tag function expects a list of tokens, so pass the variable tokens directly.

4fill in blank

hard

Fill both blanks to create a dictionary of words and their POS tags.

NLP

tokens = ['He', 'runs', 'fast']
pos_tags = nltk.pos_tag(tokens)
pos_dict = [1](pos_tags)
print(pos_dict)

# Access the POS tag of 'runs'
print(pos_dict[[2]])

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

Adict

B'runs'

C'fast'

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using list() instead of dict() to convert.

Accessing dictionary with wrong key like 'fast'.

Use dict() to convert list of tuples to dictionary, then access the word 'runs' to get its tag.

5fill in blank

hard

Fill the blanks to filter and print only nouns from the tagged tokens.

NLP

tokens = nltk.word_tokenize("Dogs bark loudly at night")
pos_tags = nltk.pos_tag(tokens)
nouns = [word for word, tag in pos_tags if tag [1] [2]]
print(nouns)

# Expected output: ['Dogs', 'night']

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

A==

Bin

CNN

D['NN', 'NNS']

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' to compare tag with a list (causes error).

Using wrong tag names or not filtering nouns.

Use 'in' to check if tag is in the list ['NN', 'NNS'] which are noun tags. 'NN' is a noun tag example.

Practice

1. What is the main purpose of part-of-speech tagging in natural language processing?

easy

A. To label each word with its grammatical role in a sentence

B. To translate text from one language to another

C. To count the number of words in a sentence

D. To generate new sentences automatically

Solution

1. Step 1: Understand the role of part-of-speech tagging

   Part-of-speech tagging assigns labels like noun, verb, adjective to each word, showing its grammatical role.

2. Step 2: Compare with other options

   Translation, word counting, and sentence generation are different NLP tasks unrelated to POS tagging.

3. Final Answer:

   To label each word with its grammatical role in a sentence -> Option A

4. Quick Check:

   POS tagging = labeling word roles [OK]

Hint: POS tagging means labeling words by their grammar role [OK]

Common Mistakes:

• Confusing POS tagging with translation
• Thinking POS tagging counts words
• Assuming POS tagging generates sentences

2. Which Python code correctly uses NLTK to perform part-of-speech tagging on the sentence 'I love AI'?

easy

A. import nltk
nltk.pos_tag(['I', 'love', 'AI'])

B. import nltk
nltk.tag_pos(['I', 'love', 'AI'])

C. import nltk
nltk.pos_tag('I love AI')

D. import nltk
nltk.pos_tag(['I love AI'])

Solution

1. Step 1: Check correct function and input type

   The correct function is pos_tag and it expects a list of words, not a string.

2. Step 2: Analyze each option

   import nltk nltk.pos_tag(['I', 'love', 'AI']) uses pos_tag with a list of words, which is correct. import nltk nltk.tag_pos(['I', 'love', 'AI']) uses a wrong function name. import nltk nltk.pos_tag('I love AI') passes a string instead of a list. import nltk nltk.pos_tag(['I love AI']) passes a list with one string, not separate words.

3. Final Answer:

   import nltk nltk.pos_tag(['I', 'love', 'AI']) -> Option A

4. Quick Check:

   pos_tag + list of words = correct syntax [OK]

Hint: Use pos_tag with a list of words, not a single string [OK]

Common Mistakes:

• Passing a string instead of a list
• Using incorrect function name
• Passing a list with one combined string

3. What is the output of the following Python code using NLTK's pos_tag?

import nltk
sentence = ['She', 'runs', 'fast']
tagged = nltk.pos_tag(sentence)
print(tagged)

medium

A. [('She', 'DT'), ('runs', 'VB'), ('fast', 'RB')]

B. [('She', 'NN'), ('runs', 'NN'), ('fast', 'JJ')]

C. [('She', 'PRP'), ('runs', 'VBZ'), ('fast', 'RB')]

D. [('She', 'PRP'), ('runs', 'VBD'), ('fast', 'RB')]

Solution

1. Step 1: Understand POS tags for each word

   'She' is a pronoun (PRP), 'runs' is a verb in present tense third person singular (VBZ), 'fast' is an adverb (RB).

2. Step 2: Match tags with options

   [('She', 'PRP'), ('runs', 'VBZ'), ('fast', 'RB')] matches these tags exactly. Other options have incorrect tags like noun (NN), determiner (DT), or past tense verb (VBD).

3. Final Answer:

   [('She', 'PRP'), ('runs', 'VBZ'), ('fast', 'RB')] -> Option C

4. Quick Check:

   Pronoun + present verb + adverb = [('She', 'PRP'), ('runs', 'VBZ'), ('fast', 'RB')] [OK]

Hint: Know common POS tags: PRP=pronoun, VBZ=verb present, RB=adverb [OK]

Common Mistakes:

• Confusing verb tenses VBZ vs VBD
• Mixing pronouns with nouns
• Mislabeling adverbs as adjectives

4. The following code throws an error. What is the most likely cause?

import nltk
sentence = 'He is happy'
tagged = nltk.pos_tag(sentence)
print(tagged)

medium

A. The nltk module is not imported correctly

B. The sentence variable should be a tuple, not a string

C. pos_tag requires a second argument specifying the language

D. The input to pos_tag should be a list of words, not a string

Solution

1. Step 1: Check input type for pos_tag

   pos_tag expects a list of words, but here a single string is passed, which causes an error.

2. Step 2: Verify other options

   nltk is imported correctly, pos_tag does not require a language argument, and input as tuple is not required.

3. Final Answer:

   The input to pos_tag should be a list of words, not a string -> Option D

4. Quick Check:

   pos_tag input must be list, not string [OK]

Hint: Always pass a list of words to pos_tag, not a string [OK]

Common Mistakes:

• Passing a string instead of a list
• Assuming pos_tag needs language argument
• Confusing input types (tuple vs list)

5. You want to tag parts of speech in a sentence but also handle unknown words gracefully. Which approach best improves POS tagging accuracy for new words?

hard

A. Manually assign tags to each unknown word before tagging

B. Use a POS tagger with a built-in model trained on large diverse text

C. Ignore unknown words during tagging to avoid errors

D. Replace unknown words with a fixed placeholder before tagging

Solution

1. Step 1: Understand handling unknown words in POS tagging

   Taggers trained on large, diverse datasets can predict tags for new words based on context and patterns.

2. Step 2: Evaluate other options

   Manually tagging unknown words is impractical, ignoring them loses information, and replacing with placeholders removes context.

3. Final Answer:

   Use a POS tagger with a built-in model trained on large diverse text -> Option B

4. Quick Check:

   Robust model with training data handles unknown words best [OK]

Hint: Choose taggers trained on big data to handle new words well [OK]

Common Mistakes:

• Trying to manually tag unknown words
• Ignoring unknown words instead of tagging
• Replacing words loses sentence meaning