Lexical analysis is an important step in natural language processing. It helps computers to break down text into smaller parts for better understanding. In this article, we will cover basic terms related to lexical analysis, the steps involved in the lexical analysis process, its benefits and drawbacks, and the types of jobs that use this process.
What Is Lexical Analysis?
Lexical analysis, also known as scanning, is an important first step in Natural Language Processing and computer science. In programming, this process involves a tool called a lexical analyzer, or lexer, which reads the source code one character at a time.
It then groups these characters into tokens, which are the smallest meaningful units in the code. Tokens can include constants like numbers and strings, operators like + and -, punctuation marks like commas and semicolons, and keywords like “if” and “while.”
After the lexer scans the text, it creates a stream of tokens. This tokenized format is necessary for the next steps in processing or compiling the program. Lexical analysis is also a good time to clean up the text by removing extra spaces or organizing certain types of data. You can think of lexical analysis as a preparation step before more complicated NLP tasks begin.
Key Term In Lexical Analysis
When learning about lexical analysis, it is essential to know some key terms. These terms will help you to understand how lexical analysis works and its role in natural language processing (NLP) and artificial intelligence. Some of the common key terms in lexical analysis are written below for your reference:
1. NLP (Natural Language Processing)
NLP is a field in computer science and artificial intelligence focused on how computers can understand and communicate with humans. The goal of NLP is to enable computers to read, understand, and respond to human language in a way that makes sense.
2. Token
A token is a group of characters treated as a single unit. Each token represents a specific meaning. In programming, tokens can include keywords, operators, and other important elements.
3. Tokenizer
A tokenizer is a tool that breaks down text into individual tokens. Each token has its own meaning. The tokenizer identifies where one token ends and another begins, which can change based on the context and rules of the language. Tokenization is usually the first step in natural language processing.
4. Lexer (Lexical Analyzer)
A lexer, or lexical analyzer, is a more advanced tool that not only tokenizes text but also classifies these tokens into categories. For example, a lexer can sort tokens into keywords, operators, and values. It is important for the next stage, called parsing, as it provides tokens to the parser for further analysis.
5. Lexeme
A lexeme is a basic unit of meaning in a language. It represents the core idea of a word. For example, the words “write,” “wrote,” “writing,” and “written” all relate to the same lexeme: “write.” In a simple expression like “5 + 6,” the individual elements “5,” “+,” and “6” are separate lexemes.
Step Involved In Lexical Analysis
Lexical analysis is a process that breaks down an input text into small parts called tokens or lexemes. This helps computers to understand the text for further analysis. Although the exact steps can vary depending on the organizational requirement and complexity of the text but most of the processes follow these basic steps:
Step 1 – Identify Tokens
The first step involved in lexical analysis is to identify individual symbols in the input. These symbols include letters, numbers, operators, and special characters. Each symbol or group of symbols is given a token type, like “number” or “operator.”
Step 2 – Assign Strings to Tokens
The lexer (a tool that performs lexical analysis) is set up to recognize and group-specific inputs. For example, if the input is “apple123,” the lexer may recognize “apple” as a word token and “123” as a number token. Similarly, keywords like “if” or “while” are categorized as specific token types.
Step 3 – Return Lexeme or Value
The lexer breaks the input into the smallest meaningful parts called lexemes. It then returns these lexemes along with their token type. This process helps the next stage of analysis which is to understand what each token represents in the text.
Difference Types Of Lexical Analysis
When choosing a method for lexical analysis, there are two main approaches: “Loop and Switch” and “Regular Expressions with Finite Automata.” Both methods help users to analyze input text by breaking it into smaller parts called tokens, making it easier for computers to process.
Let us understand each of these types of lexical analysis in brief:
1. Loop and Switch Algorithm
The loop works like reading a book, one character at a time until it reaches the end of the code. It goes through the code without missing any character or symbol, making sure each part is captured.
The switch statement acts as a quick decision-maker. After the loop reads a character or a group of characters, the switch statement decides what type of token it is, such as a keyword, number, or operator. This is similar to organizing items into separate boxes based on their type, making the code easier to understand.
2. Regular Expressions and Finite Automata
Regular expressions are rules that describe patterns in text. They help define how different tokens should look, such as an email or a phone number format. The lexer uses these rules to identify tokens by matching text against these patterns.
Finite automata are like small machines that follow instructions step-by-step. They take the rules from regular expressions and apply them to the code. If a part of the code matches a rule, it’s identified as a token. This makes the process of breaking down code more efficient and accurate.
Is Lexical Analysis A Good Choice for Text Processing?
If you are looking to use lexical analysis for text processing, it is essential to know its pros and cons. Lexical analysis is commonly used for text pre-processing in Natural Language Processing (NLP), but with advantages and uses everything has its disadvantages as well. Here are some key benefits and drawbacks of using lexical analysis:
Advantages of Lexical Analysis
- Cleans Up Data: Lexical analysis removes unnecessary elements like extra spaces or comments from the text, making the text cleaner and easier to work with.
- Simplifies Further Analysis: It breaks the text into smaller units called tokens and filters out irrelevant data, making it easier to perform other analyses like syntax checking.
- Reduces Input Size: It helps in compressing the input data by organizing it into tokens, this simplifies and speeds up the text processing.
Limitations of Lexical Analysis
- Ambiguity in Token Categorization: Lexical analysis can sometimes struggle to categorize tokens correctly which can lead to confusion and difficulties.
- Lookahead Challenges: The lexer generally requires looking ahead at the next characters to decide on token categories, which can be tricky.
- Limited Understanding: Since lexical analyzers only focus on individual tokens, they might not catch errors like incorrect sequences or misspelled identifiers in the overall text.
Who Uses Lexical Analysis?
Many careers and fields use lexical analysis in their day-to-day life. With the growth of NLP (Natural Language Processing) and artificial intelligence, the use of these skills is expanding across different industries.
One of the main careers in this area is the NLP engineer. NLP engineers have various responsibilities, such as creating NLP systems, working with speech systems in AI applications, developing new NLP algorithms, and improving existing models.
According to Glassdoor’s October 2024 data, an NLP engineer’s average annual salary in India is around ₹10 Lakh per annum
Apart from NLP engineers, There are many other related careers that may involve the use of lexical analysis in their tasks, depending on your area of focus. These careers include:
- Software Engineers
- Data Scientists
- Machine Learning Engineers
- Artificial Intelligence Engineers
- Language Engineers
- Research Engineers
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Lexical Analysis FAQs
What are the functions of a lexical analyzer?
A lexical analyzer performs several functions, such as reading the source code, removing whitespace and comments, recognizing patterns to generate tokens, and handling errors like unrecognized characters or invalid sequences.
What are tokens in lexical analysis?
Tokens are the smallest meaningful units identified during lexical analysis. They represent keywords, identifiers, operators, punctuation, or other symbols in the source code, which help define the syntax and semantics of the programming language.
What is the difference between lexical analysis and parsing?
Lexical analysis is the process of converting source code into tokens, while parsing uses these tokens to create a syntax tree or structure that represents the code’s grammatical structure. Lexical analysis focuses on individual words or symbols, whereas parsing deals with the arrangement of these tokens.
