Intermediate Topic 25 of 51 Theory & Discrete Math
Parse Trees
Trees showing how a grammar derives a string.
How it works
A parse tree is a tree representation of how a string is derived from a grammar: the root is the start symbol, internal nodes are nonterminals, children correspond to the right-hand side of the production applied and the leaves read left to right spell the string. Different derivation orders, such as leftmost and rightmost, can give the same tree. If a string has two different parse trees, the grammar is ambiguous. Abstract syntax trees simplify parse trees by dropping details.
Where it is used
Compilers use parse trees or ASTs to analyze and translate code, calculators evaluate expressions through them, and natural language processing builds syntactic trees of sentences.
Common pitfall
Grammars that ignore operator precedence and associativity produce trees that evaluate incorrectly, such as treating 1 + 2 * 3 as left to right. Parse trees can be large, so real compilers build abstract syntax trees directly. Confusing a derivation with the tree loses the structural meaning. Check ambiguity by finding strings with multiple trees.
Where it fits in the course
Parse Trees is topic 25 of 51 in Theory & Discrete Math, rated Intermediate. It follows Context-Free Grammars. The next topic is Pushdown Automata.
Practice and explore
- Open the interactive lesson, with a mini quiz.
- Take the Theory & Discrete Math quiz.
- Explore the Binary Search Tree visualizer: Insert keys and run traversals.
How to study this topic
- Read the explanation above and restate the definition in your own words.
- Open the interactive lesson and answer its mini quiz.
- Read the common pitfall, then write a short example that avoids it.
- Check your understanding in the Theory & Discrete Math quiz, and revisit the topic if you miss a question.
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Related topics
- Context-Free Grammars (Theory & Discrete Math)
- Pushdown Automata (Theory & Discrete Math)
- Pumping Lemma for Regular Languages (Theory & Discrete Math)
- Chomsky Hierarchy (Theory & Discrete Math)
- Decision Trees (AI & Machine Learning)
- Trees (Data Structures)
- Binary Trees (Data Structures)