blocks|key|183528|text|一个基本的AI工具，你可以用来做这样的事情是马尔可夫链。实际上，这并不是很难写！|type|unstyled|depth|inlineStyleRanges|entityRanges|data|183529|请参阅：http://pscode.com/vb/scripts/ShowCode.asp?txtCodeId=2031&lngWId=10|offset|length|183530|如果这还不够，您可以将WordNet同义词集存储在马尔可夫链中，而不仅仅是单词。这使您对单词的含义有了一定的了解。|183531|entityMap|0|LINK|mutability|MUTABLE|url|http://pscode.com/vb/scripts/ShowCode.asp?txtCodeId=2031&lngWId=10|1|http://wordnet.princeton.edu/^0|0|4|1U|0|0|B|7|1|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@$D|T|E|U|1|V]]|A|$]]|$1|F|3|G|5|6|7|W|8|@]|9|@$D|X|E|Y|1|Z]]|A|$]]|$1|H|3|-4|5|6|7|10|8|@]|9|@]|A|$]]]|I|$J|$5|K|L|M|A|$N|O]]|P|$5|K|L|M|A|$N|Q]]]]

A basic AI tool that you can use to do something like this is a Markov Chain. It's actually not too tricky to write!

See: <a href="http://pscode.com/vb/scripts/ShowCode.asp?txtCodeId=2031&amp;lngWId=10" rel="nofollow noreferrer">http://pscode.com/vb/scripts/ShowCode.asp?txtCodeId=2031&amp;lngWId=10</a>

If that's not enough, you might be able to store <a href="http://wordnet.princeton.edu/" rel="nofollow noreferrer" title="WordNet">WordNet</a> synsets in your Markov chain instead of just words. This gives you some sense of the meaning of the words.

blocks|key|4457715|text|为了能够重新组合文档，你必须过滤掉不好的结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4457716|这意味着：|4457717|4457718|你必须写一个程序来评估输出是否有效(从语法和句法上来说，这是你能做的最可靠的了)(这将是自然语言处理)|ordered-list-item|4457719|你将需要大量的训练数据和测试数据|4457720|你将需要注意过度训练(看看ROC曲线)|4457721|4457722|除了编写工具，您还可以：|4457723|4457724|手动为输出评分(需要很长时间才能正确训练算法)使用亚马逊机械土耳其人的|4457725|可能是个不错的选择|4457726|4457727|4457728|​|4457729|具有讽刺意味的是:计算机将很难“创造性地”组成新的东西。它的所有价值都将基于其先前的经验训练数据|4457730|entityMap^0|0|0|0|0|0|0|0|0|0|1|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|11|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|12|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|13|8|@]|9|@]|A|$]]|$1|E|3|F|5|G|7|14|8|@]|9|@]|A|$]]|$1|H|3|I|5|G|7|15|8|@]|9|@]|A|$]]|$1|J|3|K|5|G|7|16|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|17|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|18|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|19|8|@]|9|@]|A|$]]|$1|P|3|Q|5|G|7|1A|8|@]|9|@]|A|$]]|$1|R|3|S|5|G|7|1B|8|@]|9|@]|A|$]]|$1|T|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]|$1|U|3|-4|5|6|7|1D|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|1E|8|@]|9|@]|A|$]]|$1|X|3|Y|5|6|7|1F|8|@]|9|@]|A|$]]|$1|Z|3|-4|5|6|7|1G|8|@]|9|@]|A|$]]]|10|$]]

To be able to recompose a document you are going to have to have away to filter through the bad results. 

Which means:

<ol>
<li>You are going to have to write a program that can evaluate if the output is valid (grammatically and syntactically is the best you can do reliablily) (This would would NLP)</li>
<li>You would need lots of training data and test data</li>
<li>You would need to watch out for overtraining (take a look at ROC curves)</li>
</ol>

Instead of writing a tool you could: 

<ol>
<li>Manually score the output (will take a long time to properly train the algorigthm)

<ol>
<li>With this using the Amazon Mechanical Turk might be a good idea</li>
</ol></li>
</ol>

The irony of this: The computer would have a difficult time "Creatively" composing something new. All of its worth will be based on its previous experiences [training data]

blocks|key|4457605|text|在这篇文章中有一些很好的参考和阅读。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4457606|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Some good references and reading at this <a href="http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/NaturalLanguage" rel="nofollow noreferrer">Natural Language</a> article.

blocks|key|1240747|text|正如其他人所说，马尔可夫链似乎最适合于这样的任务。在Kernighan+&+Pike,+The+Practice+of+Programming,+section+3.1中可以找到实现马尔可夫链的很好的描述。Programming+Pearls中也提供了对文本生成的很好的描述。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1240748|entityMap|0|LINK|mutability|MUTABLE|url|http://books.google.com/books?id=to6M9_dbjosC&lpg=PP1&dq=practice%2520of%2520programming&pg=PA62#v=onepage&q=&f=false|1|http://books.google.pl/books?id=kse_7qbWbjsC&lpg=PA28&dq=programming%2520pearls%2520bentley%2520text%2520generator&lr=&pg=PA164#v=onepage&q=&f=false^0|Q|1M|0|2V|I|1|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]|$A|R|B|S|1|T]]|C|$]]|$1|D|3|-4|5|6|7|U|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]]]

As others said, Markov chain seems to be most suitable for such a task. Nice description of implementing Markov chain can be found in <a href="http://books.google.com/books?id=to6M9_dbjosC&amp;lpg=PP1&amp;dq=practice%20of%20programming&amp;pg=PA62#v=onepage&amp;q=&amp;f=false" rel="nofollow noreferrer">Kernighan &amp; Pike, The Practice of Programming, section 3.1</a>. Nice description of text-generating is also present in <a href="http://books.google.pl/books?id=kse_7qbWbjsC&amp;lpg=PA28&amp;dq=programming%20pearls%20bentley%20text%20generator&amp;lr=&amp;pg=PA164#v=onepage&amp;q=&amp;f=false" rel="nofollow noreferrer">Programming Pearls</a>.

blocks|key|1485378|text|有一件事，虽然不完全是你所需要的，那就是马尔可夫词链。这是我通过快速搜索找到的一个链接：http://blog.figmentengine.com/2008/10/markov-chain-code.html，但您可以通过搜索它来找到更多信息。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1485379|entityMap|0|LINK|mutability|MUTABLE|url|http://blog.figmentengine.com/2008/10/markov-chain-code.html^0|18|1O|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

One thing, though not quite what you need, would be a Markov chain of words. Here's a link I found by a quick search: <a href="http://blog.figmentengine.com/2008/10/markov-chain-code.html" rel="nofollow noreferrer">http://blog.figmentengine.com/2008/10/markov-chain-code.html</a>, but you can find much more information by searching for it.

blocks|key|1485420|text|看看http://www.nltk.org/+(自然语言工具包)，那里有很多强大的工具。他们使用Python+(不是C#)，但是Python很容易上手。至少比自然语言处理的广度和深度更容易掌握。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1485421|entityMap|0|LINK|mutability|MUTABLE|url|http://www.nltk.org/^0|2|K|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Take a look at <a href="http://www.nltk.org/" rel="nofollow noreferrer">http://www.nltk.org/</a> (Natural Language Toolkit), lots of powerful tools there. They use Python (not C#) but Python is easy enough to pick up. Much easier to pick up than the breadth and depth of natural language processing, at least.

blocks|key|1240847|text|我同意，你在创造一些有创意的东西时会遇到麻烦。你也可以在某些单词上使用关键字旋转器。您可能还想实现一个停用词筛选器来删除任何口语化的内容。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1240848|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I agree, that you will have troubles in creating something creative. You could possibly also use a keyword spinner on certain words. You might also want to implement a stop word filter to remove anything colloquial.

I am trying to find information (and hopefully c# source code) about trying to create a basic AI tool that can understand english words, grammar and context.

The Idea is to train the AI by using as many written documents as possible and then based on these documents, for the AI to create its own creative writitng in proper english that makes sense to a human.

While the idea is simple, I do realise that the hurdles are huge, any starting points or good resoueces will be appriacted.

artificial intelligence - Creative Writing

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在努力寻找有关试图创建一个基本的AI工具的信息(希望还有c#源代码)，该工具可以理解英语单词、语法和上下文。这个想法是通过使用尽可能多的书面文档来训练人工智能，然后基于这些文档，让人工智能用适当的英语创建自己的创造性写作，这对人类来说是有意义的。虽然这个想法很简单，但我确实意识到障碍是巨大的，任何起点或好的资源都会被认可。

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