• Corpus ID: 14144019

University of Amsterdam Programming Research Group Scannerless Generalized-LR Parsing

  title={University of Amsterdam Programming Research Group Scannerless Generalized-LR Parsing},
  author={Eelco Visser and Arie van Deursen and Jan van Eijck and Annius Groenink},



Disambiguating grammars by exclusion of sub-parse trees

A method is presented for disambiguation of grammars, based on the idea of excluding certain forbidden sub-parse trees, guaranteeing that the generated language is unchanged.

Deterministic parsing of ambiguous grammars

It is shown how efficient LR and LL parsers can be constructed directly from certain classes of these specifications.

Syntactic Analysis and Operator Precedence

Three increasingly restricted types of formal grammar are phrase structure Grammars, operator grammars and precedence grammar, which form models of mathematical and algorithmic languages which may be analyzed mechanically by a simple procedure based on a matrix representation of a relation between character pairs.

A Case Study in Optimizing Parsing Schemata by Disambiguation Filters

The optimization of parsing schemata, a framework for high-level description of parsing algorithms, by disambiguation filters is considered in order to find efficient parsing algorithms for declaratively specified disambigsuation methods.

The Structure of Shared Forests in Ambiguous Parsing

The Context-Free backbone of some natural language analyzers produces all possible CF parses as some kind of shared forest, from which a single tree is to be chosen by a disambiguation process that

Controlled grammatic ambiguity

Within this approach the LL and LR techniques are generalized to solve the following problems for large classes of ambiguous grammars: construction of a parser that accepts all sentences generated by the grammar, and which always terminates in linear time.

Adding Semantic and Syntactic Predicates To LL(k): pred-LL(k)

Most language translation problems can be solved with existing LALR(1) or LL(k) language tools; e.g., YACC [Joh78] or ANTLR [PDC92]. However, there are language constructs that defy almost all

Incremental analysis of real programming languages

The LR(k) language model is extended by extending the language model itself, introducing a program representation based on parse dags that is suitable for both batch and incremental analysis, and an efficient incremental parser for general context-free grammars is developed.

An efficient context-free parsing algorithm

A parsing algorithm which seems to be the most efficient general context-free algorithm known is described and appears to be superior to the top-down and bottom-up algorithms studied by Griffiths and Petrick.

Generation of formatters for context-free languages

The formatter generation approach proposed in this article can be used to automatically generate formatters that have to be programmed explicitly in other systems, and can easily be tuned in order to get the desired formatting of programs.