AI程序生成段落模式
是否有任何软件或服务或人工智能程序可以使用不同的词汇、语法规则等重建英语段落。
我的意思是说，如果源段落是
“瓜廖尔是附近一个不错的旅游胜地 到占西。詹西非常有名，因为 他们的女王拉尼·拉克西米·白 （马尼坎达纳）”
任何软件都可以生成其版本或模式，例如
“Rani Laxmi Bai (Manikandana) 是 占西女王哪个更接近 瓜廖尔很好的旅游宫殿。”
或者其他什么。我知道除非人工干预，100% 的正确性是不可能的。
Is there any software or service or AI program who can rebuild an English paragraph using different set of vocabulary, grammar rules etc.
I mean to say, if the source paragraph is
“Gwalior is a good tourist place near
to Jhansi. Jhansi is very famous due
their queen Rani Laxmi Bai
(Manikandana)”
Any software can generate its version or pattern like
“Rani Laxmi Bai (Manikandana) was the
queen of Jhansi which is nearer to a
good tourist palace Gwalior.”
Or something else. I know that 100% correctness is not possible until human intervention.
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这个人编写了一个 JavaScript 应用程序，可以生成可供分发的公司废话（他还有一个很棒的 流行词宾果生成器）。它不是人工智能，它只是简单地遵循语言规则。根据我对你的问题的理解，你不需要人工智能，你可以通过研究这个人所做的事情来学到很多东西。他在程序中植入名词、动词、形容词、副词等，并生成你的眼睛可以解析的文本（它符合语法，但不一定有意义）。如果您正在寻找一些东西来撰写论文，那么您还有很多事情要做。
从您的问题来看，您似乎也在寻找一个程序来解析英语并为前面提到的生成器生成种子数据。 Abiword 使用 这样的语法解析器，用于语法检查。我还没有深入研究它，但我认为您可以轻松地使用它来列出文本部分中包含的词性。如果您使用此程序生成种子数据，您可以将输出直接输入到另一个程序中以生成更多文本。
This guy wrote a JavaScript app that generates corporate bullshit ready for distribution (He's also got a great buzzword bingo generator). It's not AI, it just simply follows linguistic rules. From what I understand of your question, you don't need AI, you could learn a lot from just studying what this guy did. He seeds the program with nouns, verbs, adjectives, adverbs, etc and generates text that your eyes can parse (it's grammatical but it doesn't necessarily make sense). If you're looking for something to write your thesis paper, you have a lot more looking to do.
From you're question, it looks like you're also looking for a program to parse English and generate the seed data for the formerly mentioned generator. Abiword uses such a grammar parser for grammar checking. I haven't looked at it in much depth, but I figure you could easily use it to list the parts of speech contained in a section of text. If you used this program to generate the seed data you could pump the output directly into the other program to generate more text.
python NLTK 库进行自然语言解析，包括构建解析树，其中包括单词是否是动词、名词、时态等。也许您可以使用这些树并根据您想出的一些简单规则重新组织它们并验证。我认为在程序结果与源文档有很大不同之前，您不需要太多规则。一些示例规则：
http://www.nltk.org/
The python NLTK library does natural language parsing, including building parse trees which include whether a word is a verb, noun, tense etc. Perhaps you could take these trees and re-organize them according to some simple rules you come up with and verify. I don't think you would need too many rules before the results of your program are very different from the source document. Some example rules:
http://www.nltk.org/
Rapid Rewrite 是一款可以做您想做的事情的软件： http://www.rapidrewriter.com/?hop =qushy 不过它不是免费的，而且网站很糟糕。
这是另一个 - 同样的故事
http://thebestspinner.com/?id=eprocent
观看他们的视频并告诉我这不是你想要的正在寻找...
Rapid Rewrite is a software that can do what you want: http://www.rapidrewriter.com/?hop=qushy It's not free though, and the website is terrible.
Here's another one - same story
http://thebestspinner.com/?id=eprocent
watch their video and tell me that's not what you are looking for...
以下是一些用于更改书面文本的各种程序的链接。其中之一应该能够为您提供一些关于如何实现您正在寻找的内容的提示。
Here are a few links to various programs to alter written text. One of them should be able to provide you with some tips on how to implement what you're looking for.
我不同意 NLP 不是你需要遵循的道路。
但是，如果您不想走 NLP 路线，则可以在不使用 NLP 的情况下生成一些听起来不错的句子，方法是使用 n-gram 训练自定义语言模型来构建四阶或五阶模型。然后，您将使用统计概率来生成句子。
有了模型后，您可以随机选择一个起始词（在已知的句子起始词或以大写字母开头的单词的范围内），然后使用条件概率来选择下一个单词。
这篇文章中有一个简单的例子：Wordmills 即将到来...
的当然，您需要足够的培训材料才能完成此任务，因为仅对简单段落进行培训并不适合您想要重新表述段落的方式。如果不使用 NLP 技术从示例段落中检测名词、动词等（这也需要训练有素的模型），然后使用相反的句子结构重新排列它们，会比一开始就使用 NLP 更费力。
您想要做的是执行实体提取以及位置感知。不仅如此，还有实体和位置之间的关系。如果您不打算使用任何 NLP，那么这是一个非常艰巨的任务。
I disagree that NLP is not the path you need to follow.
However, if you don't want to go the NLP route, you could generate some good sounding sentences without using NLP, by training a custom language model using n-grams to build a fourth or fifth order model. You would then use statistical probability to generate your sentences.
Once you have your model, you randomly pick a starting word (in the domain of known sentence starting words, or words that begin with a capital letter), and then use conditional probablitily to pick the next word.
An easy example of this is in this article: Wordmills are coming...
Of course, you would need ample training material in order to accomplish this, as just training on a simple paragraph would not work well for the way you want to rephrase a paragraph. Without using NLP techniques to detect nouns, verbs, etc. from your sample paragraph (which would require well trained models as well), and then rearranging them using an opposite sentence structure would be more effort than just using NLP in the first place.
What you are trying to do is perform entity extraction, and also location awareness. Not only that, but relationships between entities and locations. A very tall order if you are not going to use any NLP.