我猜…它可以

寻找词语 如果没有找到，使用一些算法来尝试“猜测”这个词。

可能是来自人工智能的东西，比如Hopfield网络或反向传播网络，或者其他“识别指纹”，恢复损坏的数据，或者Davide已经提到的拼写纠正……

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.

谷歌显然建议搜索结果最好的问题，而不是拼写正确的问题。但在这种情况下，可能拼写纠正器会更可行。当然，您可以为每个查询存储一些值，基于它返回的结果有多好。

So,

You need a dictionary (english or based on your data) Generate a word trellis and calculate probabilities for the transitions using your dictionary. Add a decoder to calculate minimum error distance using your trellis. Of course you should take care of insertions and deletions when calculating distances. Fun thing is that QWERTY keyboard maximizes the distance if you hit keys close to each other.(cae would turn car, cay would turn cat) Return the word which has the minimum distance. Then you could compare that to your query database and check if there is better results for other close matches.

关于“did you mean”算法的理论可以参考《信息检索导论》第3章。它可以在网上免费下载。第3.3节(第52页)准确地回答了你的问题。为了明确回答你的更新，你只需要一个单词字典，不需要其他任何东西(包括数百万用户)。

最简单的方法是动态规划。

这是一种从信息检索中借来的算法，在现代生物信息学中大量使用，以查看两个基因序列有多相似。

最优解采用动态规划和递归。

这是一个已经解决的问题，有很多解决方案。在你找到一些开源代码之前，一直在你的周围打转。

你是说拼写检查器?如果它是一个拼写检查器而不是一个完整的短语，那么我有一个关于拼写检查的链接，其中算法是用python开发的。检查这个链接

同时，我也在从事一个项目，包括使用文本搜索数据库。我想这能解决你的问题