Introduction to Lexical Analyzer: Working Principle and Token Types
This video provides an introduction to the working principle and types of tokens recognized by a lexical analyzer in computer programming.
00:00:06 This video provides an introduction to the lexical analyzer, explaining its working principle and the types of tokens it recognizes in a high-level language code.
🎯 The lexical analyzer scans high-level language code line by line and produces tokens as output.
🔑 Tokens can be classified into several categories, such as identifiers, operators, constants, keywords, literals, punctuators, and special characters.
🔍 The lexical analyzer uses regular expressions or type 3 grammars to recognize tokens.
00:02:17 This video explains how the lexical analyzer in computer programming works. It covers recognizing keywords, identifiers, and integers using a finite state machine.
🔍 The lexical analyzer is responsible for analyzing the input and generating tokens.
🔄 The analyzing phase involves using a finite state machine to recognize keywords, identifiers, and integers.
🔢 For integers, the lexical analyzer identifies positive or negative signs followed by a sequence of digits.
00:04:30 An introduction to combining separate finite state machines into a single finite state machine using epsilon transition and NFA to DFA conversion for implementing a lexical analyzer for recognizing keywords, identifiers, and integers.
💡 Lexical analyzer combines individual finite state machines into a single finite state machine.
🔍 The lexical analyzer recognizes keywords, identifiers, and integers.
🔄 The NFA is converted to an equivalent DFA for implementation.
00:06:43 This video provides an introduction to lexical analyzer, explaining the states and transitions in a DFA for recognizing identifiers and single digit positive integers.
💡 Lexical analyzer uses states to recognize identifiers.
🔍 Transition from state 1 to 3 recognizes identifiers except 'i'.
🔢 Transition from initial state to state 4 recognizes single digit integers.
00:08:56 This video provides an introduction to lexical analyzer and discusses the working principle of deterministic finite automata (DFA) for pattern matching. It emphasizes the importance of understanding concepts such as NFA, conversion to DFA, and minimization. Please refer to our theory of computation course for detailed learning.
🔍 The lexical analyzer uses a DFA for pattern matching.
🔑 The final states of the DFA recognize different tokens: keyword, identifiers, and integers.
🚀 Knowledge of NFA, conversion to DFA, and DFA minimization is required to construct DFAs.
00:11:06 The lexical analyzer in C removes comments and white spaces from code, classifies comments as non-token elements. White spaces include space, tab, newline, vertical tab, form feed, and carriage return. It also aids in macro expansion.
🔍 The lexical analyzer identifies and removes comments from the high-level language code, using patterns such as double slashes and slash stars.
⌨️ White spaces, including spaces, tabs, new lines, and more, are recognized and eliminated by the lexical analyzer during scanning.
🔄 The lexical analyzer also supports macro expansion in high-level language code.
00:13:20 An introduction to the working principle of a lexical analyzer, which allocates values to macros in code. The lexical analyzer creates symbol table entries and produces tokens for the syntax analyzer.
The lexical analyzer assigns values to macros in the code and creates entries for identifiers in the symbol table.
The lexical analyzer produces tokens and passes them to the syntax analyzer for parsing.
The lexical analyzer and the syntax analyzer communicate frequently during the parsing process.
Want to deep dive into this video?You might also like...
Read more on Education
How Estonia Became a Model for Digital Democracy | Exponentially with Azeem Azhar
"okay, but I want GPT to perform 10x for my specific use case" - Here is how
Jurassic Park T-Rex sound design explained by Gary Rydstrom
Spotify CEO-Backed VC Firm Raises New $121 Million AI Fund
Grounded Theory - Core Elements. Part 1
"Next Level Prompts?" - 10 mins into advanced prompting
