Dependency Parsing In NLP Explained & 9 Tools With How To Tutorial

by | Oct 22, 2023 | Data Science, Natural Language Processing

What Is Dependency Parsing in NLP?

Dependency parsing is a fundamental technique in Natural Language Processing (NLP) that plays a pivotal role in understanding the grammatical structure of sentences. At its core, dependency parsing is about unravelling the relationships between words in a sentence. Analyzing how words depend on one another constructs a tree-like structure known as a dependency tree or syntactic tree, which graphically represents the syntactic and semantic relationships within the sentence.

Dependency Trees

Dependency trees are the heart of dependency parsing. They consist of nodes and directed edges. Each word in a sentence is represented as a node, and the relationships between these words are depicted as directed edges connecting the nodes. These directed edges indicate which word governs or modifies another word, establishing a clear hierarchy within the sentence.

dependency parsing of a sentence

Example of dependency parsing

Grammatical Relationships

The edges in a dependency tree represent grammatical relationships. These relationships define words’ roles in a sentence, such as subject, object, modifier, or adverbial. Here are a few common grammatical relationships:

  • Subject-Verb Relationship: In a sentence like “She sings,” the word “She” depends on “sings” as the subject of the verb.
In a sentence like "She sings," the word "She" depends on "sings" as the subject of the verb in dependency parsing
  • Modifier-Head Relationship: In the sentence “The big cat,” “big” modifies “cat,” creating a modifier-head relationship.
In the sentence "The big cat," "big" modifies "cat," creating a modifier-head relationship in dependency parsing
  • Direct Object-Verb Relationship: In “She eats apples,” “apples” is the direct object that depends on the verb “eats.”
dependency parsing object verb
  • Adverbial-Verb Relationship: In “He sings well,” “well” modifies the verb “sings” and forms an adverbial-verb relationship.
In "He sings well," "well" modifies the verb "sings" and forms an adverbial-verb relationship

Dependency parsing is the foundation for more advanced NLP tasks. It provides the necessary linguistic structure to help computers understand human language and extract valuable insights from text, making it an integral part of natural language understanding in the digital age.

What Is The Significance of Dependency Parsing?

Dependency parsing is far more than just an academic exercise in linguistics. It holds immense significance in Natural Language Processing (NLP) due to its ability to uncover the intricate grammatical relationships within sentences. Here, we delve into the multiple facets of its importance in NLP.

Syntactic Analysis

At its core, dependency parsing is a powerful tool for syntactic analysis. It helps us answer essential questions about a sentence’s structure:

  • What is the subject of the sentence?
  • Which words are objects of verbs?
  • How do adjectives modify nouns?
  • What are the adverbs modifying?

These insights are pivotal for understanding the fundamental building blocks of language and enabling machines to interpret the text as humans do.

Semantic Analysis

Dependency parsing isn’t just about the arrangement of words; it’s also about the meaning they convey. We can uncover semantic roles and connections by revealing the relationships between words. This is invaluable for tasks like sentiment analysis, where understanding the syntactic and semantic relationships between words helps discern the emotional tone of a text. In other words, dependency parsing enables machines to gauge the sentiment expressed within a sentence.

Information Extraction

Information extraction, a vital NLP task, relies heavily on dependency parsing. For instance, Named Entity Recognition (NER) aims to locate and classify named entities in text. Dependency parsing is an indispensable tool for understanding how words relate to one another, which is critical for recognizing entities and their attributes.

Machine Translation

In machine translation, which aims to convert text from one language to another, dependency parsing takes centre stage. Understanding the dependencies between words in both the source and target languages is essential for producing accurate translations. Dependency parsing ensures that the translated text maintains proper syntactic and semantic structure, resulting in coherent translations.

Dependency parsing, thus, serves as the scaffolding on which a wide range of NLP applications are built. Its ability to uncover syntactic and semantic relationships empowers machines to comprehend and process human language, making it indispensable for various linguistic and cognitive tasks. Its significance continues to grow as NLP advances and finds more applications in our daily lives.

Common Techniques in Dependency Parsing

Dependency parsing is a crucial component of Natural Language Processing (NLP) that involves the analysis of grammatical relationships between words in a sentence. Several techniques and algorithms have been developed. This section will explore some common methods used in the field.

1. Transition-Based Parsing

Transition-based parsing is an approach that models the parsing process as a sequence of actions or transitions. These transitions are used to construct the dependency tree incrementally. Commonly used transition-based parsing algorithms include:

  • ArcEager: This deterministic parsing algorithm follows a set of well-defined actions to build the dependency tree. It is known for its efficiency and has been widely used.
  • ArcStandard: Another deterministic algorithm, ArcStandard employs a set of actions to construct dependency trees. It is known for its simplicity and ease of implementation.

Transition-based parsing is often favoured for its speed and simplicity, making it suitable for real-time parsing tasks and applications requiring quick sentence analysis.

2. Graph-Based Parsing

In contrast to the transition-based approach, graph-based parsing treats dependency parsing as a global optimization problem. The goal is to find a given sentence’s most likely tree structure.

A common technique used to do this is the Maximum Spanning Tree (MST) Algorithm. MST-based parsers formulate the problem of finding a maximum spanning tree within a graph. They select the most probable dependencies by assigning weights to the edges in the graph and using algorithms like Chu-Liu-Edmonds.

Graph-based parsing is favoured for its ability to consider global information, allowing it to capture long-range sentence dependencies. However, it can be computationally more intensive compared to transition-based parsing.

3. Probabilistic Parsing

Probabilistic parsing models treat dependency parsing as a probabilistic task. These models assign probabilities to different dependency structures and choose the most likely one. Common techniques within this category include:

  • Conditional Random Fields (CRF): CRFs are often used to estimate the probability distribution over possible dependency structures. They consider features such as part-of-speech tags, word forms, and syntactic context to assign probabilities.
  • Dependency Models: Dependency models, including those using neural networks like deep learning models, estimate the likelihood of various dependency structures based on the input sentence.

Probabilistic parsing is advantageous for its ability to handle uncertainty and ambiguity in parsing. It’s beneficial when dealing with languages or sentences with complex grammatical structures.

These techniques represent the core methods for performing dependency parsing in NLP. The choice of technique often depends on the specific requirements of an application, the nature of the language being parsed, and the trade-off between computational complexity and accuracy. As NLP advances, techniques are evolving to handle various linguistic challenges and contribute to multiple NLP applications.

How Does Dependency Parsing Work?

Understanding the inner workings is fundamental to comprehending its practical applications. This section will explore how dependency parsing works in more detail.

1. Sentence Tokenization

The process begins with sentence tokenization. A document or text is split into individual sentences. Each sentence is treated as a standalone unit for parsing. Sentence tokenization is crucial because dependency parsing operates at the sentence level.

2. Part-of-Speech Tagging

Before diving into dependency relationships, each word in the sentence is assigned a part-of-speech (POS) tag. These POS tags convey each word’s grammatical category and syntactic role in the sentence. For example, common POS tags include nouns, verbs, adjectives, and adverbs. Part-of-speech tagging helps the parser understand the syntactic function of each word, which is critical for accurate dependency analysis.

3. Parsing Algorithms

The heart of dependency parsing lies in the parsing algorithms. These algorithms determine how words in the sentence depend on one another. Several parsing algorithms are used, including:

  1. ArcEager: This deterministic transition-based parsing algorithm constructs a dependency tree step by step. It defines a set of actions for transitioning between different states until the entire tree is built.
  2. ArcStandard: Another deterministic transition-based algorithm, ArcStandard, is known for its simplicity and efficiency. It employs a set of actions to construct the dependency tree.
  3. Maximum Spanning Tree (MST): MST-based parsers treat dependency parsing as an optimization problem, finding the maximum spanning tree within a graph, where words are nodes and the edges have associated weights.

4. Dependency Tree Construction

Once the parsing algorithm is applied, it starts building the dependency tree incrementally. The process begins with a root node, typically representing the sentence’s main verb. As the algorithm progresses, it adds directed edges to connect words in the sentence, establishing grammatical relationships.

Each word in the sentence becomes a node in the dependency tree, and the edges between them represent dependencies. The edge’s direction indicates the dependency’s direction; for example, a word can be the head of another word, signifying a dependency. Each word, except for the root, has a single incoming edge.

5. Types of Dependencies

Dependency parsing identifies various types of grammatical relationships, including:

  1. Subject-Verb Dependency: This represents the relationship between the subject and the governing verb. For example, in the sentence “She sings,” “She” has a subject-verb dependency with “sings.”
  2. Object-Verb Dependency: This relationship indicates the direct object of a verb. In “She eats apples,” “apples” has an object-verb dependency with “eats.”
  3. Modifier-Head Dependency: It captures how words modify other words. In “The big cat,” “big” has a modifier-head dependency with “cat.”
  4. Adverbial-Verb Dependency: This demonstrates how adverbs modify verbs. In “He sings well,” “well” has an adverbial-verb dependency with “sings.”

6. Handling ambiguity

Dependency parsing often faces challenges related to ambiguity. Sentences can have multiple valid interpretations, and the parsing algorithm needs to disambiguate between them. Contextual information from neighbouring words and advanced parsing techniques help resolve these ambiguities and determine the most likely dependency structure.

7. Output: The Dependency Tree

The final output of dependency parsing is a structured representation of the sentence in the form of a dependency tree. Each word in the sentence is represented as a node in the tree, and the directed edges between nodes capture the grammatical relationships between the words. This structured representation is invaluable for various NLP tasks, enabling machines to understand and analyze the text’s syntax and semantics more effectively.

What Are Common Applications Of Dependency Parsing?

Dependency parsing is the foundation for various Natural Language Processing (NLP) applications. By uncovering the grammatical structure and relationships within sentences, dependency parsing enables machines to comprehend and manipulate human language more effectively. In this section, we’ll explore the practical applications of dependency parsing.

1. Named Entity Recognition (NER)

Named Entity Recognition is a critical NLP task in which dependency parsing plays a pivotal role. By understanding the dependencies between words, dependency parsing helps identify named entities (e.g., persons, organizations, locations) in text. This is particularly useful for information extraction, document categorization, and knowledge graph construction.

2. Sentiment Analysis

Dependency parsing is a valuable tool for understanding the structure of sentences, which is crucial in sentiment analysis. Analyzing the dependencies between words allows sentiment analysis models to recognize how different parts of a sentence contribute to its overall sentiment. This helps determine whether a text expresses positive, negative, or neutral sentiments.

3. Question Answering

In question-answering systems, dependency parsing aids in understanding the grammatical structure of questions and passages. By identifying dependencies, the system can recognize which words are related and relevant to answer the given question. This is vital in information retrieval and retrieval-based question-answering systems.

4. Information Retrieval

Dependency parsing enhances information retrieval systems by improving the understanding of user queries. Search engines can deliver more accurate and relevant search results by parsing user queries and matching them with documents’ syntactic and semantic structures. This leads to better user experiences and more efficient retrieval of information.

5. Syntax-Based Machine Translation

Machine translation systems use dependency parsing to create more accurate translations. Understanding the dependencies between words in the source language helps generate translations that maintain the target language’s correct syntactic and semantic structure. This results in more coherent and contextually accurate translations.

6. Text Summarization

Dependency parsing can also aid in automatic text summarization. By recognizing the dependencies between words, summarization algorithms can extract the most important sentences or phrases in a document while maintaining the coherence of the summary. Dependency-based summarization techniques help in producing more coherent and contextually relevant summaries.

7. Parsing Error Detection and Correction

Dependency parsing is employed to detect and correct parsing errors in NLP systems. By comparing the parsed structure to a gold standard, errors and inaccuracies in the parse can be identified and rectified. This is crucial for improving the quality of NLP applications.

8. Grammar and Style Analysis

Dependency parsing helps analyze the grammar and style of text. It helps identify complex or erroneous sentence structures and is valuable in language teaching, proofreading tools, and natural language generation systems.

What Are The Challenges In Dependency Parsing?

While dependency parsing is a fundamental task in Natural Language Processing (NLP), it has its share of challenges. These challenges often stem from the intricacies and complexities of human language. This section will explore some common challenges faced in dependency parsing.

1. Ambiguity

One of the primary challenges in dependency parsing is dealing with ambiguity. Natural language is inherently ambiguous, and sentences can have multiple valid interpretations. For example, consider the sentence, “I saw the man with the telescope.” Is “with the telescope” modifying “saw” or “the man”? Dependency parsers must disambiguate such cases, often relying on context and other linguistic clues.

2. Multilingual Parsing

Parsing languages with different syntactic structures and word orders poses a significant challenge. Dependency parsing models trained on one language may not perform well on another due to linguistic variations. Developing multilingual parsers that can handle diverse languages is an ongoing research area.

3. Out-of-Vocabulary Words

Dependency parsers are typically trained on a finite vocabulary. When encountering out-of-vocabulary words, they may struggle to parse sentences correctly. Techniques for handling unknown words, such as morphological analysis or subword embeddings, are necessary to address this challenge.

4. Non-Projectivity

Non-projectivity refers to sentences where the dependency structure contains crossing edges or words that don’t follow a linear order. Detecting and parsing non-projective sentences is more complex than dealing with projective sentences. Advanced parsing techniques are required to handle non-projective structures effectively.

5. Parsing Errors

Even state-of-the-art parsers can make errors, especially when dealing with noisy or ungrammatical text. Identifying and correcting parsing errors is an ongoing challenge, as accurate dependency parsing is a crucial component in many NLP applications.

6. Domain Adaptation

Dependency parsers trained on general texts might not perform well on domain-specific or specialized texts. Adapting parsers to specific domains, such as medical or legal documents, remains challenging due to the need for domain-specific training data.

7. Long-Range Dependencies

Capturing long-range dependencies within a sentence is challenging, especially for transition-based parsers. Long-distance relationships between words can be crucial for understanding a sentence’s structure and meaning, and developing parsers that handle such dependencies is an active area of research.

8. Handling Ellipsis and Coreference

Dependency parsing struggles with sentences that involve ellipsis or coreference, where words or phrases refer to something mentioned earlier in the text. Resolving pronouns and other anaphoric references can be challenging for parsers.

9. Cross-Lingual Parsing

Cross-lingual parsing involves parsing a sentence in one language using a parser trained in another. This requires addressing language-specific syntax and word order differences, making cross-lingual parsing complex.

10. Resource Intensity

Developing high-accuracy dependency parsers often demands significant computational resources for training and inference. This resource intensity can be a practical challenge, particularly for resource-constrained environments.

9 Tools and Libraries for Dependency Parsing

Dependency parsing is a critical task in Natural Language Processing (NLP), and it benefits from the availability of numerous tools and libraries that simplify the process. This section will explore some popular tools and libraries.

1. SpaCy

SpaCy is an open-source NLP library that provides pre-trained models for various languages, making it a valuable choice for dependency parsing. SpaCy is known for its efficiency and ease of use. It offers accurate dependency parsing and part-of-speech tagging, making it suitable for research and production-level NLP tasks.

import spacy

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

# Parse a sentence
sentence = "The quick brown fox jumps over the lazy dog."
doc = nlp(sentence)

# Access dependency parsing information
for token in doc:
    print(token.text, token.dep_, token.head.text)

2. NLTK (Natural Language Toolkit)

The NLTK library is a comprehensive platform for building Python programs to work with human language data. It offers a wide range of NLP tools, including parsers for dependency parsing. NLTK is a popular choice for educational and research purposes.

import nltk
from nltk.parse.dependencygraph import DependencyGraph

# Sample sentence
sentence = "The quick brown fox jumps over the lazy dog."

# Dependency parsing with NLTK
dep_parser = nltk.parse.DependencyGraph
parsed = dep_parser(sentence)

# Print the dependency relations
for triple in parsed.triples():
    print(triple)

3. Stanford NLP Parser

The Stanford NLP Parser is a powerful NLP toolkit developed by Stanford University. It provides robust dependency parsing models for multiple languages. The Stanford Parser is known for its accuracy and is often used in research and production systems.

You would need to download the Stanford NLP Parser and set it up. Here’s how to use it:

from nltk.parse.stanford import StanfordDependencyParser

# Specify the path to the Stanford Parser JAR file and model file
stanford_parser_jar = '/path/to/stanford-parser.jar'
stanford_model_jar = '/path/to/englishPCFG.ser.gz'

# Create a StanfordDependencyParser object
dep_parser = StanfordDependencyParser(stanford_parser_jar, stanford_model_jar)

# Parse a sentence
sentence = "The quick brown fox jumps over the lazy dog."
result = list(dep_parser.raw_parse(sentence))

# Access dependency parsing information
for parse_tree in result:
    print(parse_tree.to_conll(4))

4. UDPipe

UDPipe is a state-of-the-art dependency parsing library that supports more than 70 languages. It’s highly efficient and is known for its accuracy in parsing. UDPipe offers pre-trained models for various languages and allows users to train custom models if needed.

To use UDPipe, you’ll need to download the appropriate model for your language. Below is an example for English.

import ufal.udpipe as udpipe

# Load the English model
model_path = "/path/to/english-ewt-ud-2.5-191206.udpipe"
model = udpipe.Model.load(model_path)

# Create a UDPipe pipeline
pipeline = udpipe.Pipeline(model, "tokenize", udpipe.Pipeline.DEFAULT, udpipe.Pipeline.DEFAULT, "conllu")

# Parse a sentence
sentence = "The quick brown fox jumps over the lazy dog."
parsed = pipeline.process(sentence)

# Access and print dependency parsing information
print(parsed)

5. AllenNLP

AllenNLP is an open-source library built on PyTorch, primarily focused on deep learning for NLP tasks. It provides pre-trained models, enabling users to incorporate state-of-the-art dependency parsers into their NLP pipelines.

To use AllenNLP, you’d need to set up the library and a model configuration. Here’s a simplified example:

from allennlp.predictors.predictor import Predictor

# Load an AllenNLP dependency parsing model
predictor = Predictor.from_path("/path/to/dependency-parsing-model")

# Parse a sentence
sentence = "The quick brown fox jumps over the lazy dog."
result = predictor.predict_json({"sentence": sentence})

# Access and print dependency parsing information
print(result['hierplane_tree']['root'])

6. Apache OpenNLP

Apache OpenNLP is an open-source NLP library offering various NLP tools, including dependency parsing. It’s a Java-based library and provides support for multiple languages. Apache OpenNLP is widely used in both research and production settings.

To use Apache OpenNLP, you need to download the OpenNLP models and set up the library. Here’s a basic example of dependency parsing:

from opennlp.tools.parser import ParserFactory, ParserModel
from opennlp.tools.parser import Parse

# Load the English model
model_path = "/path/to/en-parser-chunking.bin"
model = ParserModel(model_path)
parser = ParserFactory().create(model)

# Parse a sentence
sentence = "The quick brown fox jumps over the lazy dog."
parsed_sentence = Parse.parseParse(sentence)

# Access and print dependency parsing information
parsed_sentence.show()

7. TensorFlow and PyTorch

While these libraries are primarily associated with deep learning, researchers have developed deep learning models for dependency parsing using TensorFlow and PyTorch. These frameworks offer flexibility and the ability to train custom parsers tailored to specific applications.

8. BERT and Transformer Models

The advent of transformer-based models, such as BERT, has significantly impacted dependency parsing. Pre-trained transformer models have been fine-tuned, achieving state-of-the-art results in many cases.

9. Custom Models

Many researchers and organizations develop custom dependency parsing models tailored to their needs. This approach allows for fine-tuning models on domain-specific data or languages not covered by existing libraries.

Various tools and libraries are available, each with strengths and use cases. The library choice depends on factors like the target language, the level of accuracy required, ease of integration, and specific application needs. These tools and libraries have significantly contributed to the widespread adoption of dependency parsing in various NLP applications.

Conclusion

Dependency parsing is a fundamental and indispensable technique in Natural Language Processing (NLP). It plays a crucial role in understanding the grammatical structure of sentences, uncovering the relationships between words, and providing a foundation for various NLP applications.

Throughout this exploration, we’ve covered the critical aspects of dependency parsing, including its definition, significance, standard techniques, the parsing process, applications, challenges, and the tools and libraries available for its implementation.

In NLP, dependency parsing is the backbone for many applications, including Named Entity Recognition, Sentiment Analysis, Question Answering, Machine Translation, Information Retrieval, and many more. Its ability to unravel the syntactic and semantic relationships between words enables machines to interpret human language more effectively and accurately.

While dependency parsing brings immense value to NLP, it also presents challenges, including ambiguity, handling multilingual text, non-projectivity, and addressing parsing errors. Researchers and developers continually strive to address these challenges through advancements in parsing techniques and tools.

Furthermore, several tools and libraries are available for dependency parsing, ranging from user-friendly and efficient options like SpaCy and Stanford NLP Parser to deep learning frameworks like TensorFlow and PyTorch. The choice of tool or library depends on the specific needs of the application and the level of customization required.

As NLP continues to evolve, dependency parsing remains at the forefront of language understanding and analysis. Its ongoing development and integration into various NLP applications promise to enhance the capabilities of machines further to process, interpret, and interact with human language, making it an essential component in the journey towards more advanced and nuanced AI-powered language applications.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

Recent Articles

One class SVM anomaly detection plot

How To Implement Anomaly Detection With One-Class SVM In Python

What is One-Class SVM? One-class SVM (Support Vector Machine) is a specialised form of the standard SVM tailored for unsupervised learning tasks, particularly anomaly...

decision tree example of weather to play tennis

Decision Trees In ML Complete Guide [How To Tutorial, Examples, 5 Types & Alternatives]

What are Decision Trees? Decision trees are versatile and intuitive machine learning models for classification and regression tasks. It represents decisions and their...

graphical representation of an isolation forest

Isolation Forest For Anomaly Detection Made Easy & How To Tutorial

What is an Isolation Forest? Isolation Forest, often abbreviated as iForest, is a powerful and efficient algorithm designed explicitly for anomaly detection. Introduced...

Illustration of batch gradient descent

Batch Gradient Descent In Machine Learning Made Simple & How To Tutorial In Python

What is Batch Gradient Descent? Batch gradient descent is a fundamental optimization algorithm in machine learning and numerical optimisation tasks. It is a variation...

Techniques for bias detection in machine learning

Bias Mitigation in Machine Learning [Practical How-To Guide & 12 Strategies]

In machine learning (ML), bias is not just a technical concern—it's a pressing ethical issue with profound implications. As AI systems become increasingly integrated...

text similarity python

Full-Text Search Explained, How To Implement & 6 Powerful Tools

What is Full-Text Search? Full-text search is a technique for efficiently and accurately retrieving textual data from large datasets. Unlike traditional search methods...

the hyperplane in a support vector regression (SVR)

Support Vector Regression (SVR) Simplified & How To Tutorial In Python

What is Support Vector Regression (SVR)? Support Vector Regression (SVR) is a machine learning technique for regression tasks. It extends the principles of Support...

Support vector Machines (SVM) work with decision boundaries

Support Vector Machines (SVM) In Machine Learning Made Simple & How To Tutorial

What are Support Vector Machines? Machine learning algorithms transform raw data into actionable insights. Among these algorithms, Support Vector Machines (SVMs) stand...

underfitting vs overfitting vs optimised fit

Weight Decay In Machine Learning And Deep Learning Explained & How To Tutorial

What is Weight Decay in Machine Learning? Weight decay is a pivotal technique in machine learning, serving as a cornerstone for model regularisation. As algorithms...

0 Comments

Submit a Comment

Your email address will not be published. Required fields are marked *

nlp trends

2024 NLP Expert Trend Predictions

Get a FREE PDF with expert predictions for 2024. How will natural language processing (NLP) impact businesses? What can we expect from the state-of-the-art models?

Find out this and more by subscribing* to our NLP newsletter.

You have Successfully Subscribed!