What Is Syntax Analysis?

Syntax analysis is a technique for looking at a sentence or phrase within a language to determine the structure and grammar of the language. Syntax analysis is an important concept in software development, but it’s also a skill we practice every day. If you speak at least one language, you'll typically have an understanding of the rules of grammar and the structures. You use this knowledge to speak in phrases that make sense to others around you who also speak the language. If you want to learn another language, like Spanish, French, Python, or Java, you first need to understand the syntax of that specific language to speak or write phrases that will make sense to others. 

In computer programming, syntax helps a computer or machine understand the coding language used to write its instructions. Learn how syntax analysis works, its importance in software development and natural language processing (NLP), and explore careers that utilize this technique.

What is syntax analysis, and when can you use it?

Syntax analysis, or parsing, is a tool for analyzing language to determine its grammatical structure and rules. Syntax, or the tactical arrangement of words to make phrases that all language speakers can understand, is important in many contexts. Some of these include:  

• Everyday language: Your ability to analyze syntax plays an important role in your capacity for communicating with others and understanding what the people around you are saying. 

• Computer programming: Software developers write code in programming languages like Java, Python, or C+. They then use a compiler to translate this code into a machine language the computer can understand. Syntax allows programmers to write programs that your computer can understand.

• Linguistics: You can become a better writer by understanding grammar rules and language structure through syntax analysis. Additionally, researchers perform syntax analysis to compare languages directly and to understand the similarities and differences in how rules and ideas about grammar and language structure develop.

• NLP: The use of syntax analysis can help robots and artificial intelligence (AI) models understand and translate natural human language (e.g., English or Japanese).

How does a compiler analyze syntax?

A compiler combines two analytical processes—lexical and syntax—to help the computer understand the input language. Lexical analysis breaks the input into tokens (most commonly words). Syntax analysis then uses these tokens to build a parse tree—a diagram of sorts—to understand how the words relate to one another and to analyze whether the input follows rules of grammar and conventional language structures. 

After an input passes through lexical and syntax analysis, it undergoes semantic analysis. This step helps the computer understand what the input says, which is an important component of NLP. 

Why is syntax analysis important for natural language processing? 

NLP, a computer’s ability to understand and respond to naturally occurring human languages, relies on syntax analysis to help a computer interpret inputs and formulate responses in the appropriate language. The parse tree, created through syntax analysis, helps a computer interact with and understand the given input. Using the parse tree, an artificial intelligence (AI) model can recognize human speech and respond in kind, or it can translate that speech from one language to another. 

Types of syntax analysis

Within a compiler, syntax analysis uses two types of parsing: top-down and bottom-up. If you’re using syntax analysis in an NLP program, the two main types of parsing are constituency and dependency parsing. 

1. Parsing in compilers: Top-down and bottom-up

In a compiler, the syntax analysis process creates a parse tree—a diagram shaped like a tree in which each leaf represents a token (a word or phrase) within the input. The type of parsing you will use, top-down or bottom-up, refers to how the compiler builds the syntax tree, either starting at the bottom and working up the “trunk” or starting at the top and working down. While some top-down parse trees are easier to read and work with, bottom-up parse trees are more functional and contain more grammar structures. 

2. Parsing in natural language processing: Constituency and dependency

In NLP, syntax analysis parses an input in different ways because the AI model or NLP algorithm can analyze the input in the context of natural language as opposed to a high-level programming language. You can use dependency parsing to understand how the words in a sentence relate to one another, and you can use constituency parsing to understand the structure of the phrases.

For example, if you performed constituency parsing on the sentence: “Melissa went to the beach yesterday, and it rained.”, you might parse phrases like “Melissa went,” “to the beach,” “yesterday,” and “it rained.” In doing so, you could take a closer look at how you might be able to manipulate those phrases to change or retain the meaning of the sentence. Dependency parsing would help you understand that “to” relates to “the beach.”

Who uses syntax analysis?

Syntax analysis is an important component of software development and in the development of NLP and AI. If you want to explore a career using syntax analysis, you might consider becoming a software engineer, an NLP engineer, or an AI researcher. 

Software engineers

Average annual salary (US): $116,635 [1]

Job outlook (projected growth from 2023 to 2033): 17 percent [2]

As a software engineer, you will develop and implement software products to solve problems for your company, clients, or customers. As companies in all industries benefit from software solutions, you will have a range of potential projects you might work on. You will conduct analyses of current systems, develop and implement solutions, and educate stakeholders about how to use software programs. 

Natural language processing engineers

Average annual salary (US): $122,614 [3]

Job outlook (projected growth from 2023 to 2033): 36 percent [4]

As an NLP engineer, you will build mathematical models that teach AI models and machines to understand and react to natural human language. In this role, you will use techniques like language modeling, part-of-speech tagging, and named entity recognition to build and train AI programs. 

AI researchers

Average annual salary (US): $97,491 [5]

Job outlook (projected growth from 2023 to 2033): 26 percent [6]

As an AI researcher, you will conduct scientific research and experiments, develop new AI models, develop prototypes, or demonstrate proof of concept for uses and projects involving AI. In this role, you might also present your findings through written reports or papers or communication with stakeholders like corporate leadership. 

Read more: 6 Artificial Intelligence (AI) Jobs to Consider

How to start a career in syntax analysis

If you want to start a career that uses syntax analysis, a bachelor’s degree is a good place to start. Although you may be able to start your career as a software engineer without a formal degree, a bachelor’s is a good place to learn many of the topics and skills you’ll need to be successful in the field. Studying subjects like computer science or software engineering can help you start a career in software development, NLP, or AI research. Earning a master’s degree in an area like AI can help you qualify for more advanced roles and set you apart from your peers. 

Getting started in natural language processing with Coursera

Syntax analysis is an important process that allows you to instruct computers, either in a high-level programming language like Python or in a natural language like English. If you want to learn more about syntax analysis, consider enrolling in an online course. For example, you can explore the Natural Language Processing Specialization offered by Deep Learning.AI on Coursera. This program covers NLP attention models, probabilistic models, and more. Upon completion, gain a shareable Professional Certificate to include in your resume, LinkedIn profile, and CV.