Lookahead LR Research Articles

A Drop-in Replacement for LR(1) Table-Driven Parsing

This paper presents a construction method for a deterministic one-symbol look-ahead LR parser which allows non-terminals in the parser look-ahead. This effectively relaxes the requirement of parsing the reverse of the right-most derivation of a string/sentence. This is achieved by replacing the deterministic push down automata of LR parsing by a two-stack automata. The class of grammars accepted by the two-stack parser properly contains the LR( k ) grammars. Since the modification to the table-driven LR parsing process is relatively minor and mostly impacts the creation of the goto and action tables, a parser modified to adopt the two-stack process should be comparable in size and performance to LR parsers.

PAVT: a tool to visualize and teach parsing algorithms

A parsing algorithm visualizer is a tool that visualizes the construction of a parser for a given context-free grammar and then illustrates the use of that parser to parse a given string. Parsing algorithm visualizers are used to teach the course on compiler construction which in invariably included in all undergraduate computer science curricula. This paper presents a new parsing algorithm visualizer that can visualize six parsing algorithms, viz. predictive parsing, simple LR parsing, canonical LR parsing, look-ahead LR parsing, Earley parsing and CYK parsing. The tool logically explains the process of parsing showing the calculations involved in each step. The output of the tool has been structured to maximize the learning outcomes and contains important constructs like FIRST and FOLLOW sets, item sets, parsing table, parse tree and leftmost or rightmost derivation depending on the algorithm being visualized. The tool has been used to teach the course on compiler construction at both undergraduate and graduate levels. An overall positive feedback was received from the students with 89% of them saying that the tool helped them in understanding the parsing algorithms. The tool is capable of visualizing multiple parsing algorithms and 88% students used it to compare the algorithms.

Image Understanding Methods in Biomedical Informatics and Digital Imaging

This paper will present new possibilities for the application of image recognition methods and AI application in biomedical informatics as well as semantically oriented analysis of 2D images of coronary arteries originating from coronography examinations. In particular this paper presents the possibilities for computer analysis and recognition of local stenoses of the lumen of coronary arteries via the application of syntactic methods of pattern recognition. Such stenoses are the result of the appearance of arteriosclerosis plaques, which in consequence lead to different forms of ischemic cardiovascular diseases. Such diseases may be seen in the form of stable or unstable disturbances of heart rhythm or infarction. Analysis of the correct morphology of these artery lumina is made possible with the application of syntactic analysis and pattern recognition methods, in particular with the attribute, context-free grammar of look-ahead LR(1) type.

Lookahead lr parsing with regular right part grammars

This paper proposes a simple method for the application of a general model for lookahead parsing to regular right part grammars (RRPG's). Previous approaches have been limited to single LR grammar classes:LR(k) and LALR(k), for example. The present approach, however, makes several simple modifications to a general model of lookahead LR parsing, partitioning RRPG's into a infinite number of LR grammar classes.

A unifying model for lookahead LR parsing

We present a construction method for lookahead LR parsers that unifies several of the construction algorithms available in the literature. The model allows single, multiple and arbitrary symbol lookahead, each only when required. Three user-supplied parameters are used by the construction method; specific settings of these parameters yield well-known grammar classes such as LALR(k), SLR(k), and several subsets of the LR-Regular class, among others. Thus, rather than using several severely incompatible parser generators, or making unnatural changes to the grammar to accommodate the parsing technique, one may manipulate these parameters to obtain a suitable parser. The model captures the essence of the problem of computing lookahead for LR parsers, and provides a better understanding of the relationships among the corresponding classes of context-free grammars.

Practical arbitrary lookahead LR parsing

We present a practical technique for computing lookahead for an LR(0) parser, that progressively attempts single-symbol, multi-symbol, and arbitrary lookahead. The technique determines the amount of lookahead required, and the user is spared the task of guessing it. The class of context-free grammars defined by our technique is a subset of the LR-regular grammars; we show that unlike LR-regular, the problem of determining whether an arbitrary grammar is in the class, is decidable. When restricted to k-symbol lookahead, the technique has the power of LALR( k) parsers. It has been successfully used to resolve multi-symbol lookahead conflicts in grammars for FORTRAN, Ada, C, COBOL, and PL/I, and its performance compares favorably with that of two well-known, commercially available parser generators.

Lar(m, c, l) grammars

We present the class of LAR(M, C, L) context-free grammars: a generalization of several classes of fixed and arbitrary lookahead LR grammars that appear in the literature. The parser construction is based on three parameters M, C and L; M and C determine the type of parser, and L is the amount of lookahead. Specific settings of these parameters yield fixed-lookahead grammar classes such as LALR(k), SLR(k) and NQLALR, along with several arbitrary lookahead classes, all of which are subsets of LR-Regular. Thus both fixed and arbitrary lookahead LR techniques are described (and implemented) by one powerful model.

A general model for fixed look-ahead lr parsers

A single construction method for SLR, LALR, NQLALR and NQSLR parsers is presented. The latter two are generalizations of NQLALR(l) grammars, defined for the first time here. The construction method is based on two relations that capture the essential structure of the problem of computing fixed look-ahead for LR parsers. These relations capture both the similarities and the differences among the various techniques, which have been misunderstood in the past. Three parameters are used to specify the type of parser to be constructed and the amount of look-ahead to be used.

SLR(1) and LALR(1) parsing for unrestricted grammars

Simple LR(1) and lookahead LR(1) phrase structure grammars are defined and corresponding deterministic two-pushdown automata which parse all sentences are given. These grammars include a wide variety of grammars for non context-free languages. A given phrase structure grammar is one of these types if the parse table for the associated automaton has no multiple entries. A technique for construction of this parse table is given which in the lookahead case involves elimination of inverses in a grammar for lookahead strings for LR(0) items and computation of first sets for strings of symbols in the given grammar.

A practical arbitrary look-ahead LR parsing technique

We present a practical technique that provides an LR (0) parser with either fixed or arbitrary look-ahead. The construction algorithm is based on certain paths in the LR (0) state diagram, which must be restricted to a maximum length m . The technique determines the amount of look-ahead required, and the user is spared the task of guessing it. Instead, the user provides m . In situations where single symbol look-ahead is sufficient, the corresponding grammar class (called LAR ( m )) is identical to the NQLALR (1) class. For practical grammars that require arbitrary look-ahead, the storage requirements typically do not exceed an amount linear in the size of the corresponding LR (0) parser. The technique is shown to work for a practical programming language grammar, and has been used to solve particular cases of the PL/1 keyword problem.