Generalized Lr Parsing
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Generalized LR Parsing
Author: Masaru Tomita
language: en
Publisher: Springer Science & Business Media
Release Date: 1991-08-31
The Generalized LR parsing algorithm (some call it "Tomita's algorithm") was originally developed in 1985 as a part of my Ph.D thesis at Carnegie Mellon University. When I was a graduate student at CMU, I tried to build a couple of natural language systems based on existing parsing methods. Their parsing speed, however, always bothered me. I sometimes wondered whether it was ever possible to build a natural language parser that could parse reasonably long sentences in a reasonable time without help from large mainframe machines. At the same time, I was always amazed by the speed of programming language compilers, because they can parse very long sentences (i.e., programs) very quickly even on workstations. There are two reasons. First, programming languages are considerably simpler than natural languages. And secondly, they have very efficient parsing methods, most notably LR. The LR parsing algorithm first precompiles a grammar into an LR parsing table, and at the actual parsing time, it performs shift-reduce parsing guided deterministically by the parsing table. So, the key to the LR efficiency is the grammar precompilation; something that had never been tried for natural languages in 1985. Of course, there was a good reason why LR had never been applied for natural languages; it was simply impossible. If your context-free grammar is sufficiently more complex than programming languages, its LR parsing table will have multiple actions, and deterministic parsing will be no longer possible.
Generalized LR Parsing
Author: Masaru Tomita
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
The Generalized LR parsing algorithm (some call it "Tomita's algorithm") was originally developed in 1985 as a part of my Ph.D thesis at Carnegie Mellon University. When I was a graduate student at CMU, I tried to build a couple of natural language systems based on existing parsing methods. Their parsing speed, however, always bothered me. I sometimes wondered whether it was ever possible to build a natural language parser that could parse reasonably long sentences in a reasonable time without help from large mainframe machines. At the same time, I was always amazed by the speed of programming language compilers, because they can parse very long sentences (i.e., programs) very quickly even on workstations. There are two reasons. First, programming languages are considerably simpler than natural languages. And secondly, they have very efficient parsing methods, most notably LR. The LR parsing algorithm first precompiles a grammar into an LR parsing table, and at the actual parsing time, it performs shift-reduce parsing guided deterministically by the parsing table. So, the key to the LR efficiency is the grammar precompilation; something that had never been tried for natural languages in 1985. Of course, there was a good reason why LR had never been applied for natural languages; it was simply impossible. If your context-free grammar is sufficiently more complex than programming languages, its LR parsing table will have multiple actions, and deterministic parsing will be no longer possible.
Parsing Techniques
Author: Dick Grune
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-10-29
Parsing, also referred to as syntax analysis, has been and continues to be an essential part of computer science and linguistics. Today, parsing techniques are also implemented in a number of other disciplines, including but not limited to, document preparation and conversion, typesetting chemical formulae, and chromosome recognition. This second edition presents new developments and discoveries that have been made in the field. Parsing techniques have grown considerably in importance, both in computational linguistics where such parsers are the only option, and computer science, where advanced compilers often use general CF parsers. Parsing techniques provide a solid basis for compiler construction and contribute to all existing software: enabling Web browsers to analyze HTML pages and PostScript printers to analyze PostScript. Some of the more advanced techniques are used in code generation in compilers and in data compression. In linguistics, the importance of formal grammars was recognized early on, but only recently have the corresponding parsing techniques been applied. Also their importance as general pattern recognizers is slowly being acknowledged. This text Parsing Techniques explores new developments, such as generalized deterministic parsing, linear-time substring parsing, parallel parsing, parsing as intersection, non-canonical methods, and non-Chomsky systems. To provide readers with low-threshold access to the full field of parsing techniques, this new edition uses a two-tiered structure. The basic ideas behind the dozen or so existing parsing techniques are explained in an intuitive and narrative style, and problems are presented at the conclusion of each chapter, allowing the reader to step outside the bounds of the covered material and explore parsing techniques at various levels. The reader is also provided with an extensive annotated bibliography as well as hints and partial solutions to a number of problems. In the bibliography, hundreds of realizations and improvements of parsing techniques are explained in a much terser, yet still informal, style, improving its readability and usability. The reader should have an understanding of algorithmic thinking, especially recursion; however, knowledge of any particular programming language is not required.