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

pr 用Rust编写的快速，资源少的自然语言处理和错误纠正库。 nlprule使用资源为NLP实现了基于规则和查找的方法。 from nlprule import Tokenizer , Rules tokenizer = Tokenizer . load ( "en" ) rules = Rules . load ( "en" , tokenizer ) rules . correct ( "He wants that you send him an email." ) # returns: 'He wants you to send him an email.' rules . correct ( "I can due his homework." ) # returns: 'I can do his homework.' for s in rules . suggest ( "S

Unit2__Section__A（Grammar__Focus-4c）精品课件.ppt

LR解析器（LR（0），SLR（1），CLR（1）和LALR（1）） 是一种自底向上的解析器，用于阅读语法。 LR解析器有不同种类，其中一些是：SLR解析器，LALR解析器，Canonical LR（1）解析器。 我使用Java和GUI来实现这些解析器，以便于使用。 这很简单：首先输入无上下文语法，然后选择解析器（LR（0），SLR（1），CLR（1）和LALR（1））。 然后，您可以通过单击相应的按钮来查看已解析语法的所有属性（增强语法，第一组，跟随组，规范集合，转到表，动作表）。 另外，您可以输入不同的内容，并检查语法是否接受字符串。 这是应用程序的屏幕截图：

2020版高中英语 Unit 2 The Olympic Games Section Ⅳ Grammar课时跟踪训练 2.doc

a grammar system for shape construction