基于ProLeap ANTLR4的COBOL解析器 这是一个基于的COBOL解析器，它为COBOL代码生成抽象语法树（AST）和抽象语义图（ASG）。 AST以语法树结构表示普通的COBOL源代码。 ASG通过语义分析从AST生成，并提供数据和控制流信息（例如，变量访问）。 EXEC SQL，EXEC SQLIMS和EXEC CICS语句被提取为文本。 该解析器是受测试驱动开发的，通过了NIST测试套件，已成功应用于银行和保险业的许多COBOL文件。 :dizzy: 如果您喜欢我们的工作，请加星号。 例子 输入：COBOL代码 Identification Division. Program-ID. HELLOWORLD. Procedure Division. Display "Hello world". STOP RUN. 输出：抽象语法树（AST） (startRul

西班牙语语法 西班牙语语法。 西班牙语和英语的比较。 语法表。 西班牙语语法备忘单。 网站： :

jMotif-GI：语法推断 为和实现Sequtur（在线）和Re-Pair（离线）语法归纳算法。 此代码在下。 有关已实现算法的更多信息： [1] Nevill-Manning，CG和Witten，IH， ，《人工智能研究杂志 ，第7卷，第67-82页，（1997年）。 [2]新泽西州拉尔森； Moffat，A。， ，数据压缩会议，1999年。会议记录。 DCC '99，vol。，pp.296,305，1999年3月29-31日。 引用这项工作： 如果您在学术工作中使用此实现，请引用我们的： [引文] Senin，P.，Lin，J.，Wang，X.，Oates，T.，Gandhi，S.，Boedihardjo，AP，Chen，C.，Frankenstein，S.，Lerner，M.， ，ECML / PKDD Conference，2014年。 1.0建筑 该代码是用Ja

LL(1)语法分析器 Author -XingruiYi 实现功能 -绘制LL(1)语法分析表 -可以消除直接左递归 输入要求 -在Input.txt文件中进行输入 -每一个终结符，非终结符，|，->，用单个空格分开 -其中#表示空字符 -非终结字符末尾不能带“'”(为实现直接左递归消除专用符号) -其中Input2.txt为测试不含左递归语法的测试输入，需要修改文件名为Input.txt为之进行测试 -TABLE_Output.txt文件为输出文件

This indispensable guide provides explanations and examples for all the important areas of grammar. #Author:L G.Alexander # Paperback: 374 pages # Publisher: Longman Publishing Group (June 1988),Twentieth impression 2003 # Language: English # ISBN-10: 0582558921 # ISBN-13: 978-0582558922 About Author: Louis Alexander was born in London in 1932 He was educated at Godalming Grammar School and London University He taught English in Germany (1954-56) and Greece (1956-65), where he was Head of the English Department of the Protypon Lykeion, Athens He was adviser to the Deutscher Volkshochschulverband (1968-78) and contributed to the design of two important English examinations in German Adult Education He was a member of the Council of Europe Committee on Modern Language Teaching (1973-78) and is one of the authors of The Threshold Level (1975) and Waystage (1977) These modern syllabuses are the basis of many communicative language courses He is also one of the authors of English Grammatical Structure (1975), a basic syllabus for grading structures for teaching/learning purposes In 1986-88 he was adviser to the University of Cambridge Local Examinations Syndicate for the Cambridge Certificate in English for International Communication Louis Alexander is best known as the author of innovative works like First Things First (1967), which set new standards in course-design He has written Courses, such as New Concept English (1967), Look, Listen and Learn (1968-71), Target (1972-74), Mainline (1973-81), Follow Me (1979-80) and Plain English (1987-88) Language Practice Books such as A First Book in Comprehension (1964), Question and Answer (1967) and For and Against (1968) Readers, such as Operation Mastermind (1971), K's First Case (1975), Dangerous Game (1977) and Foul Play (1983) He created the blueprint for the self-study series in modern languages, Survive (1980-83) and has published language courses in the field of computer-assisted language learning The Longman English Grammar is the culmination of more than thirty years' work in English as a foreign language

语言工具 LanguageTool是一款开放源代码校对软件，适用于英语，法语，德语，波兰语，俄语以及。 它会发现许多简单的拼写检查器无法检测到的错误。 （ ） 有关更多信息，请参见我们的主页，为 ， 和 。 LGPL 2.1或更高版本免费提供LanguageTool。 码头工人 对于社区贡献的Docker文件，请尝试以下项目之一： ） ） 会费 描述了如何贡献错误检测规则。 请参阅问题以获取开始的问题。 有关更多技术细节，请参见。 脚本化安装和构建 要使用脚本进行安装或构建，只需键入： curl -L https://raw.githubusercontent.com/languagetool-org/languagetool/master/install.sh | sudo bash 如果希望有更多选择，请下载install.sh脚本。 使用选

This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and non-terminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple And–Or graph representation where each Or-node points to alternative sub-configurations and an And-node is decomposed into a number of components. This representation supports recursive top-down/bottom-up procedures for image parsing under the Bayesian framework and make it convenient to scale up in complexity. Given an input image, the image parsing task constructs a most probable parse graph on-the-fly as the output interpretation and this parse graph is a subgraph of the And–Or graph after * Song-Chun Zhu is also affiliated with the Lotus Hill Research Institute, China. making choice on the Or-nodes. (iii) A probabilistic model is defined on this And–Or graph representation to account for the natural occurrence frequency of objects and parts as well as their relations. This model is learned from a relatively small training set per category and then sampled to synthesize a large number of configurations to cover novel object instances in the test set. This generalization capability is mostly missing in discriminative machine learning methods and can largely improve recognition performance in experiments. (iv) To fill the well-known semantic gap between symbols and raw signals, the grammar includes a series of visual dictionaries and organizes them through graph composition. At the bottom-level the dictionary is a set of image primitives each having a number of anchor points with open bonds to link with other primitives. These primitives can be combined to form larger and larger graph structures for parts and objects. The ambiguities in inferring local primitives shall be resolved through top-down computation using larger structures. Finally these primitives forms a primal sketch representation which will generate the input image with every pixels explained. The proposal grammar integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. Finally the paper presents three case studies to illustrate the proposed grammar.

编译原理语法分析实习，武汉大学计算机学院，课程报告。

solidity-antlr4：ANTLR4的固态语法

语法检查器 该代码的目的是使用深度学习技术纠正简单的语法错误，更具体地说，是使用注意机制对序列模型进行延迟的序列。 数据集 由于没有用于语法校正的开源数据集，因此我决定使用一种简单的技术向包含不符合语法要求的句子的数据集添加语法插补。 这是我发现的最大的会话书面英语集，在语法上基本上是正确的，超过30万行。 给定这样的文本样本，下一步是生成在训练期间使用的输入输出对。 这是通过以下方式完成的： 从数据集中绘制示例句子。 随机应用某些扰动后，将输入序列设置为此句子。 将输出序列设置为不受干扰的句子。 其中在步骤（2）中应用的扰动旨在引入小的语法错误，我们希望模型学习纠正。 到目前为止，这些干扰仅限于： 减去文章（a，an，the） 用其对应的同一个替换一些普通的同音字（例如，用“ there”替换“ their”，用“ than”替换“ then”） 在此项目中，每种干扰都会