Dependency parsing in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the spaCy library for dependency parsing.

NLP

import [1]

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

Aspacy

Btensorflow

Cnltk

Dsklearn

Attempts:

3 left

💡 Hint

Common Mistakes

Importing nltk instead of spaCy

Using sklearn which is for machine learning, not NLP

spaCy is a popular library for natural language processing, including dependency parsing.

2fill in blank

medium

Complete the code to load the English language model in spaCy.

NLP

nlp = spacy.load('[1]')

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

Aes_core_news_sm

Ben_core_web_sm

Cde_core_news_sm

Dfr_core_news_sm

Attempts:

3 left

💡 Hint

Common Mistakes

Using a French or German model for English text

Misspelling the model name

The English small model in spaCy is named 'en_core_web_sm'.

3fill in blank

hard

Fix the error in the code to get the dependency label of the first token.

NLP

doc = nlp('I love AI')
label = doc[0].[1]

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

Alemma_

Bpos_

Cdep_

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using pos_ instead of dep_

Using text which gives the token string, not the dependency label

The dependency label of a token is accessed with the dep_ attribute in spaCy.

4fill in blank

hard

Fill both blanks to create a dictionary of tokens and their dependency labels.

NLP

dep_dict = {token.[1]: token.[2] for token in doc}

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

Atext

Bdep_

Cpos_

Dlemma_

Attempts:

3 left

💡 Hint

Common Mistakes

Using pos_ instead of dep_ for dependency labels

Using lemma_ instead of text for token keys

We use text for the token string and dep_ for its dependency label.

5fill in blank

hard

Fill all three blanks to print each token, its head token, and the dependency relation.

NLP

for token in doc:
    print(f"Token: {token.[1], Head: {token.[2].[3]")

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

Atext

Bhead

Ddep_

Attempts:

3 left

💡 Hint

Common Mistakes

Using dep_ instead of text for printing token strings

Trying to print head without accessing its text attribute

We print the token's text, the head token object, and the head token's text.

Practice

1. What is the main purpose of dependency parsing in Natural Language Processing?

easy

A. To show how words in a sentence are connected

B. To translate sentences into another language

C. To count the number of words in a sentence

D. To generate new sentences automatically

Solution

1. Step 1: Understand dependency parsing

   Dependency parsing analyzes sentence structure by showing relationships between words.

2. Step 2: Compare options

   Only To show how words in a sentence are connected correctly describes this purpose; others describe different NLP tasks.

3. Final Answer:

   To show how words in a sentence are connected -> Option A

4. Quick Check:

   Dependency parsing = word connections [OK]

Hint: Dependency parsing = word connection map [OK]

Common Mistakes:

• Confusing parsing with translation
• Thinking it counts words only
• Mixing with sentence generation

2. Which of the following is the correct way to access the dependency label of a token using spaCy in Python?
doc = nlp('I love cats')

easy

A. doc[1].dep_

B. doc.dep_[1]

C. doc[1].dependency

D. doc.dep[1]

Solution

1. Step 1: Recall spaCy token attributes

   In spaCy, each token has a dep_ attribute accessed by doc[index].dep_.

2. Step 2: Check options for correct syntax

   Only doc[1].dep_ uses correct attribute and indexing syntax.

3. Final Answer:

   doc[1].dep_ -> Option A

4. Quick Check:

   Token dependency label = doc[index].dep_ [OK]

Hint: Use token.dep_ to get dependency label [OK]

Common Mistakes:

• Using wrong attribute name like dep or dependency
• Trying to index dep_ attribute
• Confusing token and doc object

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

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('She eats an apple')
for token in doc:
    print(f'{token.text} -> {token.dep_}')

medium

A. She -> det
eats -> dobj
an -> nsubj
apple -> ROOT

B. She -> dobj
eats -> nsubj
an -> ROOT
apple -> det

C. She -> ROOT
eats -> nsubj
an -> dobj
apple -> det

D. She -> nsubj
eats -> ROOT
an -> det
apple -> dobj

Solution

1. Step 1: Understand dependency roles in sentence

   In 'She eats an apple', 'eats' is the main verb (ROOT), 'She' is subject (nsubj), 'an' is determiner (det), 'apple' is direct object (dobj).

2. Step 2: Match roles to output

   She -> nsubj eats -> ROOT an -> det apple -> dobj correctly matches each word to its dependency label.

3. Final Answer:

   She -> nsubj eats -> ROOT an -> det apple -> dobj -> Option D

4. Quick Check:

   Subject = nsubj, Verb = ROOT, Object = dobj [OK]

Hint: Main verb is ROOT; subject is nsubj; object is dobj [OK]

Common Mistakes:

• Mixing subject and object labels
• Confusing determiner with object
• Assuming first word is ROOT

4. Identify the error in this spaCy dependency parsing code:

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Dogs bark loudly')
for token in doc:
    print(token.dep)

medium

A. Incorrect model name in spacy.load

B. doc should be a list, not a spaCy Doc object

C. Missing underscore in token.dep_ attribute

D. print statement syntax is wrong

Solution

1. Step 1: Check token attribute usage

   spaCy tokens use dep_ (with underscore) to get dependency label as string; dep without underscore returns an integer ID.

2. Step 2: Verify code correctness

   Code uses token.dep which prints integer IDs, not readable labels; likely intended to print labels, so underscore is missing.

3. Final Answer:

   Missing underscore in token.dep_ attribute -> Option C

4. Quick Check:

   Use token.dep_ for labels, not token.dep [OK]

Hint: Use token.dep_ (with underscore) for readable labels [OK]

Common Mistakes:

• Using token.dep instead of token.dep_
• Assuming doc is wrong type
• Thinking print syntax is incorrect

5. You want to extract all verbs and their direct objects from a sentence using dependency parsing. Which approach is best?

hard

A. Use only token text without parsing dependencies

B. Find tokens with POS tag 'VERB' and check their children with dependency label 'dobj'

C. Extract tokens with POS tag 'NOUN' ignoring dependencies

D. Select tokens with dependency label 'nsubj' only

Solution

1. Step 1: Understand task requirements

   We want verbs and their direct objects, so we need to find verbs and check which tokens depend on them as direct objects (dobj).

2. Step 2: Evaluate options

   Find tokens with POS tag 'VERB' and check their children with dependency label 'dobj' correctly finds verbs and their dobj children. Others ignore dependencies or focus on subjects or nouns only.

3. Final Answer:

   Find tokens with POS tag 'VERB' and check their children with dependency label 'dobj' -> Option B

4. Quick Check:

   Verbs + dobj children = correct extraction [OK]

Hint: Look for verbs and their dobj children in dependency tree [OK]

Common Mistakes:

• Ignoring dependency labels
• Selecting only subjects
• Using POS tags without dependencies