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.

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

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.

前段时间我发现了一篇文章:《如何编写拼写更正》，作者是Peter Norvig(谷歌公司的研究总监)。

这是一本关于“拼写纠正”主题的有趣读物。例子是用Python写的，但是很清楚，很容易理解，而且我认为算法可以很容易 翻译成其他语言。

下面是该算法的简短描述。 该算法包括两个步骤，准备和单词检查。

步骤1:准备-设置word数据库

最好是你能使用实际的搜索词和它们的出现。 如果你没有，你可以用大量的文本来代替。 计算每个单词的出现次数(流行度)。

步骤2。单词检查-找到与被检查的单词相似的单词

相似意味着编辑距离很低(通常是0-1或0-2)。编辑距离是将一个单词转换为另一个单词所需的插入/删除/更改/交换的最小数量。

从上一步中选择一个最流行的词，并建议它作为更正(如果不是这个词本身的话)。

这是一个老问题，我很惊讶没有人建议OP使用Apache Solr。

Apache Solr是一个全文搜索引擎，除了许多其他功能，还提供拼写检查或查询建议。从文档中可以看到:

默认情况下，Lucene拼写检查器首先根据 分由弦距计算和秒由频 (如有)在索引内的建议。

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)。