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Titlebook: Generalized LR Parsing; Masaru Tomita Book 1991 Springer Science+Business Media New York 1991 Parsing.algorithms.complexity.grammar.hidden

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書(shū)目名稱(chēng)Generalized LR Parsing
編輯Masaru Tomita
視頻videohttp://file.papertrans.cn/383/382219/382219.mp4
圖書(shū)封面Titlebook: Generalized LR Parsing;  Masaru Tomita Book 1991 Springer Science+Business Media New York 1991 Parsing.algorithms.complexity.grammar.hidden
描述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 languag
出版日期Book 1991
關(guān)鍵詞Parsing; algorithms; complexity; grammar; hidden markov model; logic programming; natural language; program
版次1
doihttps://doi.org/10.1007/978-1-4615-4034-2
isbn_softcover978-1-4613-6804-5
isbn_ebook978-1-4615-4034-2
copyrightSpringer Science+Business Media New York 1991
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Experiments with GLR and Chart Parsing,ject for the development of a machine translation (MT) system for translating avalanche forecast bulletins from German to French. The design of the MT system requires controlled input and no post-editing of the translated texts. The parsing experiment had as a goal to select the most suitable parsin
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GLR Parsing in Time O(,,),s efficient than algorithms for restricted CF parsing, e.g., the LL, operator precedence, and LR algorithms (Aho and Ullman, 1972), because they must simulate a multi-path, nondeterministic pass over their inputs using some form of search, typically, goal-driven. While many of the general algorithms
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Book 1991sing 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 languag
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