Dependency parsing in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is dependency parsing in natural language processing?

Dependency parsing is the process of analyzing the grammatical structure of a sentence by establishing relationships between "head" words and words that modify those heads, called "dependents." It helps understand how words connect to each other.

beginner

What is a head word in dependency parsing?

A head word is the main word in a phrase or sentence that other words depend on. For example, in "eats an apple," "eats" is the head because "an" and "apple" depend on it.

intermediate

What does a dependency relation represent?

A dependency relation shows the type of connection between a head word and its dependent, like subject, object, or modifier. It explains the role of the dependent word in the sentence.

intermediate

How does dependency parsing differ from constituency parsing?

Dependency parsing focuses on word-to-word relationships, showing how words depend on each other. Constituency parsing breaks sentences into nested groups or phrases. Dependency parsing is often simpler and more direct for understanding sentence structure.

advanced

Name a common algorithm used for dependency parsing.

The arc-standard and arc-eager algorithms are popular for dependency parsing. They build the dependency tree step-by-step by adding arcs between words.

In dependency parsing, what does a 'dependent' word do?

A It is the main word that others rely on

B It modifies or depends on a head word

C It forms a phrase with other words

D It is always a noun

A dependent word depends on a head word and modifies or relates to it.

Which of these is a typical dependency relation?

A Sentence

B Paragraph

C Subject

D Chapter

Subject is a common dependency relation showing the doer of an action.

What is the output of a dependency parser?

A A tree showing word dependencies

B A list of words only

C A summary of the text

D A translation of the sentence

Dependency parsing produces a tree structure showing how words depend on each other.

Which parsing method focuses on phrases rather than word-to-word links?

A Dependency parsing

B Semantic parsing

C Lexical parsing

D Constituency parsing

Constituency parsing breaks sentences into nested phrases.

Which algorithm is commonly used in dependency parsing?

A Arc-standard

B K-means

C Decision tree

D Naive Bayes

Arc-standard is a popular algorithm for building dependency trees.

Explain in your own words what dependency parsing is and why it is useful.

Think about how words in a sentence connect to each other.

Describe the difference between dependency parsing and constituency parsing.

One looks at connections between words, the other at groups of words.

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