• Articles
  • Tutorials
  • Interview Questions

What is Parsing in NLP: Its Types and Techniques

What is Parsing in NLP: Its Types and Techniques

Parsing is the key to a world of NLP applications, where computers read, write, and interpret text like never before. In this blog, we will discover what parsing in NLP is, along with its types and applications. But before moving ahead, ponder over a thought- What drives the magic behind parsing in NLP?

Do you want to learn more about the fascinating field of NLP? Check out our YouTube video to learn more about NLP!

What is Parsing in NLP?

Parsing is the process of examining the grammatical structure and relationships inside a given sentence or text in natural language processing (NLP). It involves analyzing the text to determine the roles of specific words, such as nouns, verbs, and adjectives, as well as their interrelationships. 

This analysis produces a structured representation of the text, allowing NLP computers to understand how words in a phrase connect to one another. Parsers expose the structure of a sentence by constructing parse trees or dependency trees that illustrate the hierarchical and syntactic relationships between words. 

This essential NLP stage is crucial for a variety of language understanding tasks, which allow machines to extract meaning, provide coherent answers, and execute tasks such as machine translation, sentiment analysis, and information extraction.

If you want to know more about ‘What is Natural Language Processing?’ you can go through this Natural Language Processing Using Python course!

Types of Parsing in NLP

Types of Parsing in NLP

The types of parsing are the core steps in NLP, allowing machines to perceive the structure and meaning of the text, which is required for a variety of language processing activities. There are two main types of parsing in NLP which are as follows: 

Get 100% Hike!

Master Most in Demand Skills Now !

Syntactic Parsing

Syntactic parsing deals with a sentence’s grammatical structure. It involves looking at the sentence to determine parts of speech, sentence boundaries, and word relationships. The two most common approaches included are as follows:

  • Constituency Parsing: Constituency Parsing builds parse trees that break down a sentence into its constituents, such as noun phrases and verb phrases. It displays a sentence’s hierarchical structure, demonstrating how words are arranged into bigger grammatical units.
  • Dependency Parsing: Dependency parsing depicts grammatical links between words by constructing a tree structure in which each word in the sentence is dependent on another. It is frequently used in tasks such as information extraction and machine translation because it focuses on word relationships such as subject-verb-object relations.

Semantic Parsing

Semantic parsing goes beyond syntactic structure to extract a sentence’s meaning or semantics. It attempts to understand the roles of words in the context of a certain task and how they interact with one another. Semantic parsing is utilized in a variety of NLP applications, such as question answering, knowledge base populating, and text understanding. It is essential for activities requiring the extraction of actionable information from text.

Parsing Techniques in NLP

The fundamental link between a sentence and its grammar is derived from a parse tree. A parse tree is a tree that defines how the grammar was utilized to construct the sentence. There are mainly two parsing techniques, commonly known as top-down and bottom-up

Read our blog on Syntax Analysis in Compiler Design, also known as parsing, for a detailed guide.

Top-Down Parsing

  • A parse tree is a tree that defines how the grammar was utilized to construct the sentence. Using the top-down approach, the parser attempts to create a parse tree from the root node S down to the leaves. 
  • The procedure begins with the assumption that the input can be derived from the selected start symbol S. 
  • The next step is to find the tops of all the trees that can begin with S by looking at the grammatical rules with S on the left-hand side, which generates all the possible trees.
  • Top-down parsing is a search with a specific objective in mind. 
  • It attempts to replicate the initial creation process by rederiving the sentence from the start symbol, and the production tree is recreated from the top down. 
  • Top-down, left-to-right, and backtracking are prominent search strategies that are used in this method. 
  • The search begins with the root node labeled S, i.e., the starting symbol, expands the internal nodes using the next productions with the left-hand side equal to the internal node, and continues until leaves are part of speech (terminals).
  • If the leaf nodes, or parts of speech, do not match the input string, we must go back to the most recent node processed and apply it to another production. 

Let’s consider the grammar rules:

Sentence = S = Noun Phrase (NP)  + Verb Phrase (VP) + Preposition Phrase (PP)

Take the sentence: “John is playing a game”, and apply Top-down parsing

 Step1 top-down

If part of the speech does not match the input string, backtrack to the node NP. 

Step2 top-down

Part of the speech verb does not match the input string, backtrack to the node S, since PNoun is matched.

The top-down technique has the advantage of never wasting time investigating trees that cannot result in S, which indicates it never examines subtrees that cannot find a place in some rooted tree.

Bottom-Up Parsing

  • Bottom-up parsing begins with the words of input and attempts to create trees from the words up, again by applying grammar rules one at a time. 
  • The parse is successful if it builds a tree rooted in the start symbol S that includes all of the input. Bottom-up parsing is a type of data-driven search. It attempts to reverse the manufacturing process and return the phrase to the start symbol S. 
  • It reverses the production to reduce the string of tokens to the beginning Symbol, and the string is recognized by generating the rightmost derivation in reverse.
  • The goal of reaching the starting symbol S is accomplished through a series of reductions; when the right-hand side of some rule matches the substring of the input string, the substring is replaced with the left-hand side of the matched production, and the process is repeated until the starting symbol is reached. 
  • Bottom-up parsing can be thought of as a reduction process. Bottom-up parsing is the construction of a parse tree in postorder.

Considering the grammatical rules stated above and the input sentence “John is playing a game”,

The bottom-up parsing operates as follows:

Interested in learning Artificial Intelligence? Go through this Artificial Intelligence Tutorial!

Parsers and Its Types in NLP

As previously stated, a parser is essentially a procedural interpretation of grammar. It searches through the space of a variety of trees to find the best tree for the provided text. Let’s have a look at some of the accessible parsers below. 

  • Recursive Descent Parser
    • A top-down parser that iteratively breaks down the highest-level grammar rule into subrules is known as a recursive descent parser. It is frequently implemented as a set of recursive functions, each of which handles a certain grammatical rule.
    • This style of parser is frequently employed in hand-crafted parsers for simple programming languages and domain-specific languages.
  • Shift-Reduce Parser
    • A shift-reduce parser is a sort of bottom-up parser that starts with the input and builds a parse tree by performing a series of shift (transfer data to the stack) and reduction (apply grammar rules) operations.
    • Shift-reduce parsers are used in programming language parsing and are frequently used with LR (Left-to-right, Rightmost derivation) or LALR (Look-Ahead LR) parsing techniques
  • Chart Parser
    • A chart parser is a sort of parsing algorithm that efficiently parses words by using dynamic programming and chart data structures. To reduce unnecessary work, it stores and reuses intermediate parsing results.
    • Early parser is a type of chart parser that is commonly utilized for parsing context-free grammars.
  • Regexp Parser
    • A regexp (regular expression) parser is used to match patterns and extract text. It scans a larger text or document for substrings that match a specific regular expression pattern.
    • Text processing and information retrieval tasks make extensive use of regexp parsers.

Each of these parsers serves a different purpose and has its own set of benefits and drawbacks. The parser chosen is determined by the nature of the parsing task, the grammar of the language being processed, and the application’s efficiency requirements.

Get ready for the high-paying Data Scientist jobs with these Top Data Science Interview Questions and Answers!

How Does the Parser Work?

The first step is to identify the sentence’s subject. The parser divides the text sequence into a group of words that are associated with a phrase. So, this collection of words that are related to one another is referred to as the subject.

Syntactic parsing and parts of speech are context-free grammar structures that are based on the structure or arrangement of words. It is not determined by the context.

The most important thing to remember is that grammar is always syntactically valid, even if it may not make contextual sense.

Applications of Parsing in NLP

Parsing is a key natural language processing approach for analyzing and comprehending the grammatical structure of natural language text. Parsing is important in NLP for various reasons. Some of them are mentioned below:

Syntactic Analysis: Parsing helps in determining the syntactic structure of sentences by detecting parts of speech, phrases, and grammatical relationships between words. This information is critical for understanding sentence grammar.

Named Entity Recognition (NER): NER parsers can detect and classify entities in text, such as people’s, organizations, and locations’ names, among other things. This is essential for information extraction and text comprehension.

Semantic Role Labeling (SRL): SRL parsers determine the semantic roles of words in a sentence, such as who is the “agent,” “patient,” or “instrument” in a given activity. It is essential for understanding the meaning of sentences.

Machine Translation: Parsing can be used to assess source language syntax and generate syntactically correct translations in the target language. This is necessary for machine translation systems such as Google Translate.

Question Answering: Parsing is used in question-answering systems to help break down a question into its grammatical components, allowing the system to search a corpus for relevant replies.

Text Summarization: Parsing is the process of extracting the essential syntactic and semantic structures of a text, which is necessary for producing short and coherent summaries.

Information Extraction: Parsing is used to extract structured information from unstructured text, such as data from resumes, news articles, or product reviews.


In NLP, parsing is the foundation for understanding the structure of human language. Parsing is the bridge connecting natural language to computational understanding, serving diverse applications like syntactic analysis, semantic role labeling, machine translation, and more. As NLP technology advances, parsing will continue to be a critical component in improving language understanding, making it more accessible, responsive, and valuable in a wide range of applications.

Know more about ‘What is Natural Language Processing?’ and clear your doubts and queries from our experts in our Artificial Intelligence Community!

Course Schedule

Name Date Details
Data Scientist Course 01 Jun 2024(Sat-Sun) Weekend Batch
View Details
Data Scientist Course 08 Jun 2024(Sat-Sun) Weekend Batch
View Details
Data Scientist Course 15 Jun 2024(Sat-Sun) Weekend Batch
View Details

About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.