从一组给定的单词中做出有意义的句子 [英] Making a meaningful sentence from a given set of words

问题描述

我正在开发一个程序，该程序需要从给定的一组单词中创建语法正确的句子.在这里，我将把字符串列表的输入传递给程序，并且我的输出应该是一个有意义的句子，用这些单词和一些其他必要的单词组成.例如

I am working on a program that needs to create a sentence that is grammatically correct from a given set of words. Here I will be passing an input of a list of strings to the program and my output should be a meaningful sentence made with those words, and a few other words that are necessary. Eg.

Input: {'You' , 'House' , 'Beautiful'}
Output: 'Your house is beautiful' (or) 'you house is beautiful' 
Input: {'Father' , 'Love' , 'Child'}
Output: 'The father loves the child'

我该如何使用NLTK和(或)机器学习来实现这一目标?

How do I implement this with NLTK and(or) Machine Learning?

关于我应该如何处理的任何建议?我已经准备好提出最疯狂的想法.谢谢！ :)

Any suggestions as to how I should go about this? I'm ready to even the most wildest ideas. Thanks! :)

推荐答案

在这种情况下，您可以应用 n-gram模型.这个想法是一个句子

In this case you can apply an n-gram model. The idea is that a sentence

我非常喜欢NLP.

I like NLP very much.

得到以下3-克:

1. <s> I like
2. I like NLP
3. like NLP very
4. NLP very much
5. very much </s>

1. <s> I like
2. I like NLP
3. like NLP very
4. NLP very much
5. very much </s>

然后您将其视为概率模型P(word3 | word1 word2).

Then you think of it as a probability model P(word3 | word1 word2).

所以您的工作将是:

1. 彼此获取大量n个单词的数据(例如，我认为 https://books.google.com /ngrams 具有下载选项)
2. 对于一组给定的单词，找到所有仅包含这些单词的n-gram.
3. 找到最可能的组合.

1. Get a lot of data of n words after each other (e.g. I think https://books.google.com/ngrams has a download option)
2. For a given set of words, find all n-grams which contain only those words
3. Find the most likely combination.

请注意:

• n至少应为3
• n越大，得到的可能性就越大"退避"，因为您没有数据(但是n-gram可能存在并且很有意义)
• 甚至n = 5的数据已经非常多了

• n should be at least 3
• the bigger n gets, the more likely it gets that you have to "back off" as you don't have data (but the n-gram might exist and make sense)
• even n=5 is already VERY much data

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