Aho–Ullman algorithms for parsing
E672059
Aho–Ullman algorithms for parsing are foundational compiler-construction techniques that efficiently analyze and translate the syntactic structure of programming languages based on formal grammar theory.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Aho–Ullman algorithms for parsing canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7540013 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aho–Ullman algorithms for parsing Context triple: [Alfred V. Aho, notableConcept, Aho–Ullman algorithms for parsing]
-
A.
Thompson's algorithm for regular expression matching
Thompson's algorithm for regular expression matching is a classic method that converts regular expressions into nondeterministic finite automata (NFAs) to enable efficient pattern matching in text processing.
-
B.
Van Wijngaarden grammars
Van Wijngaarden grammars are a highly expressive formal grammar formalism, introduced for defining complex programming language syntax and semantics, notably used in the specification of ALGOL 68.
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C.
Augmented Backus–Naur Form
Augmented Backus–Naur Form (ABNF) is a standardized, extended version of Backus–Naur Form used to formally specify the syntax of languages and protocols, notably in Internet and communication standards.
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D.
Compilers: Principles, Techniques, and Tools
Compilers: Principles, Techniques, and Tools is a foundational computer science textbook that systematically covers the theory and practice of compiler design and implementation.
-
E.
Thompson's algorithm
Thompson's algorithm is a classic computer science method for converting regular expressions into nondeterministic finite automata (NFAs), widely used in pattern matching and lexical analysis.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aho–Ullman algorithms for parsing Target entity description: Aho–Ullman algorithms for parsing are foundational compiler-construction techniques that efficiently analyze and translate the syntactic structure of programming languages based on formal grammar theory.
-
A.
Thompson's algorithm for regular expression matching
Thompson's algorithm for regular expression matching is a classic method that converts regular expressions into nondeterministic finite automata (NFAs) to enable efficient pattern matching in text processing.
-
B.
Van Wijngaarden grammars
Van Wijngaarden grammars are a highly expressive formal grammar formalism, introduced for defining complex programming language syntax and semantics, notably used in the specification of ALGOL 68.
-
C.
Augmented Backus–Naur Form
Augmented Backus–Naur Form (ABNF) is a standardized, extended version of Backus–Naur Form used to formally specify the syntax of languages and protocols, notably in Internet and communication standards.
-
D.
Compilers: Principles, Techniques, and Tools
Compilers: Principles, Techniques, and Tools is a foundational computer science textbook that systematically covers the theory and practice of compiler design and implementation.
-
E.
Thompson's algorithm
Thompson's algorithm is a classic computer science method for converting regular expressions into nondeterministic finite automata (NFAs), widely used in pattern matching and lexical analysis.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
compiler-construction technique
ⓘ
context-free grammar parsing method ⓘ parsing algorithm family ⓘ |
| application |
semantic analysis frameworks in compilers
ⓘ
syntax-directed translation ⓘ |
| author |
Alfred V. Aho
ⓘ
Jeffrey D. Ullman ⓘ |
| basedOn |
context-free grammars
ⓘ
formal grammar theory ⓘ |
| coreConcept |
FIRST sets
ⓘ
FOLLOW sets ⓘ items and item sets in LR parsing ⓘ parse tables ⓘ recursive-descent parsing ⓘ shift-reduce parsing ⓘ viable prefixes ⓘ |
| describedIn |
"Compilers: Principles, Techniques, and Tools"
ⓘ
linked to:
Compilers: Principles, Techniques, and Tools
"Principles of Compiler Design" ⓘ
linked to:
Compilers: Principles, Techniques, and Tools
|
| enables |
detection of syntax errors
ⓘ
translation of source programs to intermediate representations ⓘ |
| field |
compiler construction
ⓘ
formal language theory ⓘ programming languages ⓘ |
| goal |
construction of efficient parsers
ⓘ
syntax analysis of programming languages ⓘ |
| hasType |
LL parsing algorithms
ⓘ
LR parsing algorithms ⓘ bottom-up parsing algorithms ⓘ operator-precedence parsing algorithms ⓘ predictive parsing algorithms ⓘ top-down parsing algorithms ⓘ |
| impact | standardized teaching of parsing in computer science curricula ⓘ |
| influenced |
design of many programming language compilers
ⓘ
modern parser generators such as Bison ⓘ modern parser generators such as Yacc ⓘ |
| namedAfter |
Alfred V. Aho
ⓘ
Jeffrey D. Ullman ⓘ |
| property |
aim to run in linear time in the length of the input
ⓘ
designed for deterministic context-free languages ⓘ |
| relatedTo |
attribute grammars
ⓘ
finite automata for lexical analysis ⓘ pushdown automata for parsing ⓘ syntax-directed definitions ⓘ |
| timePeriod | 1970s ⓘ |
| usedIn |
compiler front ends
ⓘ
interpreters ⓘ parser generators ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Aho–Ullman algorithms for parsing Description of subject: Aho–Ullman algorithms for parsing are foundational compiler-construction techniques that efficiently analyze and translate the syntactic structure of programming languages based on formal grammar theory.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.