使用Levenshtein距离，然后创建一个度量树(或Slim树)来索引单词。 然后运行1-Nearest Neighbour查询，就得到了结果。

嗯…我认为谷歌使用他们庞大的数据语料库(互联网)来做一些严肃的NLP(自然语言处理)。

例如，他们拥有来自整个互联网的大量数据，以至于他们可以计算出三个单词序列出现的次数(称为三元组)。因此，如果他们看到一个句子:“pink frugr concert”，他们可以看到它的点击率很少，然后在语料库中找到最有可能的“pink * concert”。

他们显然只是做了Davide Gualano所说的一种变化，所以一定要阅读那个链接。谷歌当然使用它所知道的所有网页作为一个语料库，这使得它的算法特别有效。

这是我找到的最好的答案，由谷歌的研究总监Peter Norvig实施和描述的拼写纠正器。

如果你想了解更多这背后的理论，你可以阅读他书中的章节。

该算法的思想基于统计机器学习。

我的猜测是，他们使用了Levenshtein距离算法和他们收集的大量关于正在运行的搜索数据的组合。他们可以提取一组与输入的搜索字符串的Levenshtein距离最短的搜索，然后选择结果最多的搜索。

通常，产品拼写纠正器会使用几种方法来提供拼写建议。一些人:

Decide on a way to determine whether spelling correction is required. These may include insufficient results, results which are not specific or accurate enough (according to some measure), etc. Then: Use a large body of text or a dictionary, where all, or most are known to be correctly spelled. These are easily found online, in places such as LingPipe. Then to determine the best suggestion you look for a word which is the closest match based on several measures. The most intuitive one is similar characters. What has been shown through research and experimentation is that two or three character sequence matches work better. (bigrams and trigrams). To further improve results, weigh a higher score upon a match at the beginning, or end of the word. For performance reasons, index all these words as trigrams or bigrams, so that when you are performing a lookup, you convert to n-gram, and lookup via hashtable or trie. Use heuristics related to potential keyboard mistakes based on character location. So that "hwllo" should be "hello" because 'w' is close to 'e'. Use a phonetic key (Soundex, Metaphone) to index the words and lookup possible corrections. In practice this normally returns worse results than using n-gram indexing, as described above. In each case you must select the best correction from a list. This may be a distance metric such as levenshtein, the keyboard metric, etc. For a multi-word phrase, only one word may be misspelled, in which case you can use the remaining words as context in determining a best match.

几年前我在这方面看到过一些东西，所以可能已经改变了，但显然他们是通过分析相同用户在短时间内提交非常相似的查询的日志开始的，并根据用户如何纠正自己使用机器学习。