The Dependency Parsing in NLP Secret That Every Language AI Engineer Should Know

Dependency Parsing in NLP is a critical task that involves constructing a tree-like structure to explicitly represent the grammatical relationships between words. This structure highlights the connections between elements such as subject-verb, object-verb, and modifier-head.

To perform Dependency Parsing in NLP various techniques like transition-based parsing and graph-based parsing are employed. Tools such as spaCy and Stanford CoreNLP facilitate these processes.

In this blog, we will explore the techniques and applications of Dependency Parsing in NLP, shedding light on its importance in understanding language structure.

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What is Dependency Parsing in NLP? Key Concepts and Role

Dependency parsing is a natural language processing (NLP) technique that seeks to establish grammatical relationships between words in a sentence. The objective is to identify the syntactic structure of the sentence by representing it as a dependency tree.

Each word in a sentence is linked to another word in the sentence (usually the “head”), creating a hierarchy that shows how the words depend on each other for meaning. It is widely used in tasks like machine translation in NLP, question answering, and sentiment analysis. Here, understanding the relationships between words helps in interpretation.

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Dependency parsing divides the sentence into head and dependent for better interpretation. 

Let’s explore these terms briefly:

• Head: It is the central word that governs other words in the sentence. It determines the syntactic role of its dependent words. 

  For instance, in the sentence "The dog sat on the mat", “sat” is the head because it is the main verb that governs the sentence's structure.

• Dependent: This word depends on another (the head) to express its full meaning, relying on the head to establish context.

  For instance, in the sentence "The dog sat on the mat", “The,” “dog,” “on,” “the,” and “mat” are dependents, as they depend on the head word “sat” to complete their syntactic relationships.

Dependency parsing works by representing a sentence in the form of a dependency tree. Each node represents a word, and edges represent dependencies between those words. It consists of components such as root, node, and edges.

Here’s a look at key concepts involved in dependency parsing.

1. Dependency Tree Structure

The dependency tree structure consists of the following components. You can understand the concept through the following sentence: "The dog sat on the mat".

• Nodes: These represent individual words in the sentence. For example, each word in "The dog sat" would be a node.
• Edges: These are directed links between words, showing which word governs another. For instance, an edge from “sat” to “dog” indicates that “sat” is the head of the subject “dog.”
• Root: The root is the topmost word in the tree, often representing the main verb or the core of the sentence. In simple sentences, the root is usually the main verb, like “sat” in the example sentence.

2. Grammatical Relationships

Grammatical relationships represent the common relation between different parts of the sentence. Here are some important relationships.

• Subject-Verb: The subject of a sentence is usually the noun or noun phrase that acts as the verb. 

  For example, in “She runs,” "She" is the subject and “runs” is the verb (head).

• Modifier-Head: Modifiers provide additional information about other words. 

  In "The big dog barked loudly," “big” modifies “dog,” and "loudly" modifies “barked.” These modifiers are dependents of their respective heads.

• Object-Verb (O-V): The object receives the action of the verb, usually in transitive verb structures. 

  In the sentence "She ate the apple”, “Apple” is the object, dependent on “ate” (verb).

• Preposition-Object (P-O): In a prepositional phrase, the preposition governs the object it introduces.

  For instance, consider the sentence, "The cat sat on the mat." “On” is the preposition, and “mat” is its object.

• Auxiliary-Verb (Auxiliary-Head): An auxiliary verb helps to form different tenses, moods, or voices and depends on the main verb. 

  For example, in "She is running", “Is” is the auxiliary verb modifying the main verb “running.”

Dependency parsing identifies word-level grammatical relationships, with each word depending on another to form a tree. In contrast, constituency parsing breaks a sentence into sub-phrases, representing its hierarchical structure.

Also Read: Natural Language Processing Projects Ideas Topics for Beginners

Next, let’s explore the key dependency tags used in parsing to better understand these relationships.

What are the Key Dependency Tags in Dependency Parsing?

In dependency parsing, key dependency tags represent the grammatical roles words play in a sentence, helping you understand their relationships. By learning these tags, you can analyze sentence structure more effectively and gain insights into language patterns.

Dependency tags are used to label the grammatical relationships between words in a sentence, forming a tree structure where each word (except the root) depends on another word. For example, in the sentence “She eats an apple,” the tag nsubj (nominal subject) links “She” to the verb “eats,” while obj (object) connects “apple” to the same verb. 

These tags help machines—and humans—understand who is doing what to whom. In NLP tasks like machine translation, sentiment analysis, or question answering, dependency tags provide context that enhances accuracy by revealing the roles and relationships that surface-level word order alone might miss.

Methods of Dependency Parsing in NLTK

NLTK is a Python library that can handle various natural language processing (NLP) tasks, including tokenization, lemmatization, stemming, parsing, and part-of-speech tagging. Probabilistic Projective Dependency Parser and the Stanford Parser are the two common methods used in NLTK.

Here’s how these two methods are used for dependency parsing:

1. Probabilistic Projective Dependency Parser

It is a transition-based parser that converts the parsing task as a sequence of decisions, applying transitions to a stack of words. 

It builds a dependency tree by moving words between a stack and buffer, applying actions to shift words or add dependency links (e.g., between a noun and its verb). Using a probabilistic model, it selects the most likely action based on learned probabilities.

However, a probabilistic projective dependency parser makes mistakes, thereby affecting its widespread use. Other limitations include the following.

• Projectivity Constraint: The parser assumes that dependencies do not cross each other. This can be a limitation for handling non-projective sentences (e.g., in languages with freer word order).
• Accuracy: It struggles with complex sentence structures or sentences that don't closely align with the training data.
• Speed: Slower than simpler rule-based parsers, especially when processing large corpora.

2. Stanford Parser

The Stanford parser supports machine learning techniques to produce both dependency trees and phrase structure trees for a given sentence. Trained on a wide range of linguistic data, it can perform syntactic tasks like identifying the subject and object of a sentence.

Apart from English, this parser supports languages like Arabic, Spanish, Italian, German, Mandarin, and many more.

To understand how the Stanford parser operates, consider the following example: 

"Raj quickly solved the complex problem in the lab."

• solved is the root verb.
• Raj is the subject (nsubj) of "solved".
• quickly is an adverb modifying "solved" (advmod).
• problem is the direct object (dobj) of "solved".
• the is a determiner modifying problem (det).
• complex is an adjective modifying problem (amod).
• in is a preposition (prep) linking to lab.
• lab is the object of the preposition in (pobj).

Now, let’s implement the sentence using the Stanford Dependency Parser in NLTK. Here’s the code snippet for the operation.

import os
from nltk.parse.stanford import StanfordDependencyParser

# Set environment variables (adjust paths to your setup)
os.environ['STANFORD_PARSER'] = '/path/to/stanford-parser'
os.environ['STANFORD_MODELS'] = '/path/to/stanford-parser'

# Initialize the StanfordDependencyParser
parser = StanfordDependencyParser(
    path_to_jar='/path/to/stanford-parser.jar',
    path_to_models_jar='/path/to/stanford-parser-models.jar'
)

# Example sentence
sentence = "Raj quickly solved the complex problem in the lab."

# Parsing the sentence
result = parser.raw_parse(sentence)

# Display the dependency tree
for dep_tree in result:
    dep_tree.tree().pretty_print()

Output:

When you run this code, the output will be a visual representation of the dependency tree. 

           solved
          /      \
      Raj     problem
        |        /    \
     quickly   the   complex
                  \
                   in
                    \
                    lab

Now that you’ve discovered how dependent parsing is implemented in NLTK, let’s understand the concept of constituency parsing. 

Constituency Parsing in NLP: An Overview

Constituency parsing analyzes a sentence into its hierarchical structure of constituents or sub-phrases. Each constituent is made up of a group of words that functions as a unit, such as noun phrases (NP), verb phrases (VP), and prepositional phrases (PP).

It is effective for text generation tasks, while dependency parsing excels in syntactic disambiguation for tasks like information extraction.

Here’s how constituency parsing works.

Consider a sentence: "The cat sat under the tall tree in the garden."

Breaking this sentence into sub-phrases, you get:

• The cat → Noun Phrase (NP) (subject)
• sat → Verb Phrase (VP) (verb)
• under the tall tree → Prepositional Phrase (PP) (modifier of the verb)
• in the garden → Prepositional Phrase (PP) (modifier of the verb)

The entire structure is represented in a tree, where each phrase is a node, and the tree shows how words group into larger constituents like NP, VP, and PP.

Here’s how the parse tree would look for this example:

• S: Sentence (root node of the tree)
• NP: Noun Phrase ("The cat")
• VP: Verb Phrase ("sat under the tall tree in the garden")
• PP: Prepositional Phrase ("under the tall tree" and "in the garden")
• Det: Determiner ("The", "the")
• N: Noun ("cat", "tree", "garden")
• V: Verb ("sat")
• P: Preposition ("under", "in")

While constituency parsing provides a detailed hierarchical breakdown of sentences, dependency parsing focuses on direct relationships between individual words.

Here’s the difference between constituency parsing and dependency parsing.

Now that you've seen what dependency parsing is in NLP and its difference from constituency parsing, let's explore how it works.

How Does Dependency Parsing Work? A Quick Overview

Dependency parsing identifies the grammatical structure of a sentence by establishing direct syntactic relationships between words. Each word (except the root) is linked to another word (its "head") that governs it, forming dependency links.

Here are the steps involved in the dependency parser in NLP.

1. Sentence Tokenization: Breaks the sentence into individual tokens (words and punctuation marks). This identifies the elements that will be analyzed.

2. Part-of-Speech (POS) Tagging: Each token is assigned a part-of-speech (POS) tag, such as noun, verb, adjective, etc. POS tagging helps identify the syntactic role of each word in the sentence.

3. Dependency Parsing Algorithms: Parsing algorithms analyze the sentence structure and create the dependency tree. These algorithms determine which words are heads and which words depend on them.

• Transition-based parsers build the tree by moving words between a stack and buffer and applying actions.
• Graph-based parsers analyze all possible relationships between words and choose the most likely dependencies based on a model.

4. Constructing Dependency Trees: In a dependency tree, the root is typically the main verb or the action, and all other words depend on it in a hierarchical structure.

For a better understanding of the process, let’s consider a sample sentence and construct a dependency tree using the above steps.

Example: "The cricket team won the match in Mumbai."

1. Step 1: Tokenization of Sentence

Split the sentence into individual words (tokens). You get the following tokens.

["The", "cricket", "team", "won", "the", "match", "in", "Mumbai", "."]

2. Step 2: Part-of-Speech (POS) Tagging

Assigns a grammatical category to each word in the sentence. Here’s how the POS tags would look:

• The → Determiner (DT)
• cricket → Noun, singular (NN)
• team → Noun, singular (NN)
• won → Verb, past tense (VBD)
• the → Determiner (DT)
• match → Noun, singular (NN)
• in → Preposition (IN)
• Mumbai → Proper noun, singular (NNP)
• . → Punctuation (.)

3. Step 3: Dependency Parsing

• Graph-based Parser:
  • Identify the root word (usually the main verb, here it's "won").
  • Look at all possible relationships between the words and evaluate which ones have the highest probability, such as "team" as the subject of "won" or "match" as the object of "won".
  • Build a dependency tree where each word (except the root) depends on another word (its head), selecting the most likely dependencies for the sentence structure.
• Transition-based Parser:
  • The algorithm would start with a stack containing the first word and a buffer containing the remaining words.
  • Initially, the buffer contains all words of the sentence, and the stack starts empty.
  • The parser will shift words from the buffer to the stack until it identifies the subject ("team") and the verb ("won") and then reduce them to build dependencies, such as linking "team" to "won".
  • Shift and reduce to identify and link all other dependencies like "match" to "won", "cricket" to "team", and "Mumbai" to "in".

4. Step 4: Build a Dependency Tree

• Root Identification: The root is the main verb or action of the sentence. In this example, "won" is the root because it represents the core action of the sentence.
• Identifying Dependencies: Each word (except the root) is linked to another word that governs it (its "head"). For example, "team" depends on "won" because it’s the subject of the verb, and "match" depends on "won" as the object.
• Building the Tree: Words are connected based on their syntactic roles (subject, object, modifier, etc.). 

Words like "cricket" modify "team", and "the" modifies "match". Similarly, "in" governs "Mumbai", specifying the location of the action.

• Final Tree Structure: The tree structure is built from the root down to all dependent words, with arrows (dependencies) showing the relationships.

Based on the analysis, a dependency tree will be constructed. Here is a dependency tree constructed based on the transition-based parser algorithm.

          won
          / | \
      team  match in
      /      |    |
  cricket   the  Mumbai

Here: 

• won is the root of the tree (main action).
• team is the subject of the verb won.
• cricket is an adjective modifier of a team.
• A match is the direct object of winning.
• the is a determiner modifying match.
• in is a preposition that connects to match.
• Mumbai is the object of the preposition in.

Implementation using spaCy:

You can implement dependency parsing tasks using a Python library like spaCy, which is widely used for NLP tasks.

Here’s the code snippet for this example.

import spacy

# Load the spaCy model for English
nlp = spacy.load("en_core_web_sm")

# Example sentence
sentence = "The cricket team won the match in Mumbai."

# Step 1: Tokenization and Step 2: POS Tagging
doc = nlp(sentence)

# Step 3: Dependency Parsing and Step 4: Display Dependency Tree
for token in doc:
    print(f"Word: {token.text}, POS: {token.pos_}, Head: {token.head.text}, Dep: {token.dep_}")

# Visualizing the Dependency Tree (optional, requires running in Jupyter/Colab)
# doc.visualize()  # Uncomment if running in a notebook that supports visualization

Explanation:

• nlp = spacy.load("en_core_web_sm"): Loads the pre-trained English model from spaCy.
• doc = nlp(sentence): Processes the input sentence to tokenize, tag POS, and perform dependency parsing.
• token.pos_: Displays the part-of-speech tag for each word.
• token.head.text: Shows the head word for each token.
• token.dep_: Shows the syntactic dependency relationship for each token.

Output:

To visualize the output in Jupyter, run the following code:

from spacy import displacy

# Visualize the dependency tree
displacy.render(doc, style="dep", jupyter=True)

You can see an interactive visual tree similar to this:

           won
          / | \
      team  match in
      /      |    |
  cricket   the  Mumbai

Dependency parsers must also handle ambiguities and long-range dependencies. Ambiguities occur when a word has multiple possible heads or roles, while long-range dependencies arise when distant words are syntactically related.

Modern parsers use advanced algorithms, such as graph-based and transition-based, that use context and machine learning to link words accurately.

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Now that you've seen how dependency parsing in NLP analyzes sentences for language tasks, let's explore its other benefits in detail.

Why is Dependency Parsing Important? Key Benefits of NLP

Dependency parsing is essential in NLP as it analyzes the syntactic structure of sentences, identifying relationships between words. This enhances machine translation by preserving syntactic connections between source and target languages, ensuring accurate meaning transfer. 
It also aids tasks like sentiment analysis, question answering, and information extraction by disambiguating word meanings based on context.

Here are the benefits of dependency parsing in NLP:

1. Improved Sentiment Analysis:

In sentiment analysis, accurate identification of relationships between subjects, verbs, and objects is crucial. Sentiment analysis is based on how words relate to each other in a sentence.

Example: Customer feedback analysis feature in e-commerce platforms (e.g., Amazon, Flipkart) or brand sentiment analysis in social media monitoring tools (e.g., Brandwatch, Hootsuite).

2. Text Summarization

Helps identify key parts of a sentence (subject, object, and verb) and understand their interdependencies, allowing to condense information while maintaining key relationships.

Example: News aggregation platforms (e.g., Google News) automatically generate summaries from articles for readers based on key dependencies.

3. Accurate Machine Translation

Dependency parsing ensures syntactic relationships between words in one language are preserved when translating to another language.

Example: Translation services in global businesses (e.g., Google Translate) ensure that translation does not violate the meaning of the sentence.

Also Read: Evolution of Language Modelling in Modern Life

4. Improves Information Extraction

Helps extract structured information from unstructured text by clearly identifying relationships between entities.

Example: Use in legal document analysis in law firms, where extracting critical data (e.g., dates, parties involved, contract terms) from contracts is important.

5. Enhances Named Entity Recognition (NER)

Systems can understand which words in a sentence refer to people, places, or organizations and how they are related to other words.

Example: Allows customer support automation tools, such as chatbots, to recognize names while giving responses.

6. Precise Answering

Question-answering systems can identify the subject, predicate, and object, which are essential for understanding and answering questions correctly.

Example: In Virtual assistants, dependency parsing ensures more precise answers to user queries by understanding the sentences correctly.

7. Facilitates Multilingual NLP Tasks: 

In multilingual applications, dependency parsing in NLP is essential for ensuring that syntactic structures are maintained across different languages. Since different languages have varying syntactic norms, dependency parsing helps in mapping these structures in a way that retains meaning, even in cross-lingual contexts.

Example: For multilingual content moderation systems that monitor social media platforms in different languages, dependency parsing allows for consistent interpretation and response generation.

8. Improved Coreference Resolution: 

Coreference resolution involves identifying when different words refer to the same entity in a sentence or across sentences. Dependency parsing in NLP helps by providing a clearer picture of syntactic dependencies, allowing for more accurate identification of pronouns and the nouns they refer to and enhancing overall comprehension.

Example: In narrative text, dependency parsing assists in resolving who “he” or “she” refers to, ensuring more accurate interpretation in summarization or question-answering systems.

9. Improved Relationship Extraction in Knowledge Graphs: 

Dependency parsing in NLP aids in extracting specific relationships between entities in text, which is essential for building and updating knowledge graphs. By understanding how different entities relate to each other, NLP systems can construct more meaningful and semantically rich knowledge graphs that are crucial for tasks like search engine optimization and content recommendation.

Example: Knowledge graph creation for search engines, where dependency parsing helps in identifying relationships like "CEO of" or "located in" to build a network of related facts.

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Now that you’ve explored the benefits offered by dependency parsing in NLP, let’s examine the tools and technologies that help in performing these functions.

Integrating Dependency Parsing with Other NLP Tasks

Dependency parsing enhances NLP tasks by revealing how words relate grammatically. In NER, it clarifies entity roles. In machine translation, it preserves structure across languages. For sentiment analysis, it links opinions to targets more accurately. In question answering and information extraction, it identifies who did what to whom. 

By adding structure-aware context, dependency parsing boosts precision across the NLP pipeline.

1. Combining Dependency Parsing with Named Entity Recognition (NER)

Named Entity Recognition (NER) is a key task in Natural Language Processing (NLP), focused on identifying named entities in text. Integrating dependency parsing with NER can enhance the accuracy of both tasks.

• Dependency parsing in NLP offers valuable insights into sentence structure, helping to identify entities and their relationships more effectively.
• Conversely, NER can inform dependency parsing decisions by pinpointing important entities within a sentence.

Several approaches to combining dependency parsing with NER include:

• Joint Modeling: Both tasks are modeled together using a unified model.
• Multi-Task Learning: A single model is trained to handle both tasks simultaneously.
• Pipelining: The output of one task is used as input for the other.

2. Using Dependency Parsing to Improve Semantic Role Labeling (SRL)

Semantic Role Labeling (SRL) identifies the roles played by entities in a sentence. Dependency parsing in NLP can significantly enhance SRL by providing structural insights and clarifying relationships between entities.

• Dependency parsing in NLP can identify predicate-argument structures, which are crucial for SRL.
• The dependency parse tree helps pinpoint the arguments of predicates and their associated roles.

3. Enhancing Machine Translation with Dependency Parsing

Machine translation (MT) translates text from one language to another. It enables machine translation to achieve better results by incorporating sentence structure and entity relationships.

• Dependency parsing in NLP helps reveal grammatical structures that inform translation decisions.
• The dependency parse tree enables the rearrangement of words in the target language, preserving meaning and grammatical correctness.

Also Read: Machine Translation in NLP: Examples, Flow & Models

Now, let’s look at some of the tools and techniques used for dependency parsing.

What are the Tools and Techniques for Dependency Parsing?

Tools like spaCy and Benepar are used for tasks like tokenizing sentences and constructing dependency trees. Techniques like probabilistic parsing help determine how to build the most accurate dependency tree.

Here are the different tools and techniques used for dependency parsing in NLP.

After exploring the tools used for dependency parsing in NLP, let’s understand the underlying techniques that enable these tools to efficiently build accurate dependency trees.

Here are the techniques used in dependency parsing in NLP.

Also Read: Top NLP Interview Questions to Excel in 2025 Interviews

Now that you've explored the tools and techniques used in dependency parsing for NLP, let's look at how you can deepen your understanding of this technology.

How Can upGrad Help You Master Dependency Parsing in NLP?

Dependency parsing in natural language processing (NLP) is used in language processing tasks, such as sentiment analysis and customer feedback, social. 

To create advanced systems for such tasks, upGrad’s machine learning courses can be beneficial, helping you develop skills in dependency parsing and other key techniques.

Here are some courses offered by upGrad to help you build your knowledge in this field:

• Introduction to Natural Language Processing
• Fundamentals of Deep Learning and Neural Networks
• Introduction to Generative AI
• Advanced Prompt Engineering with ChatGPT
• Executive Diploma in Machine Learning and AI

Do you need help deciding which courses can help you in dependency parsing? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.

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