我一直在为一个投资组合管理工具开发一个内部网站。有很多文本数据，公司名称等。我对一些搜索引擎的能力印象深刻，它们可以非常快速地回答“你的意思是:xxxx”。
我需要能够智能地接受用户的查询，并不仅响应原始搜索结果，而且还响应“您的意思是?”当有一个极有可能的替代答案等
我正在开发ASP。NET (VB -别跟我过不去!)］
更新: 好吧，在没有数百万“付费用户”的情况下，我该如何模仿这种模式?
为每个“已知”或“正确”的术语生成拼写错误并执行查找? 还有其他更优雅的方法吗?
当前回答
关于你的问题，如何在没有大量数据的情况下模仿行为——为什么不使用谷歌收集的大量数据呢?下载谷歌的sarch结果拼写错误的单词，并搜索“Did you mean:”在HTML中。
我猜现在这叫做混搭:-)
其他回答
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。
关于你的问题，如何在没有大量数据的情况下模仿行为——为什么不使用谷歌收集的大量数据呢?下载谷歌的sarch结果拼写错误的单词，并搜索“Did you mean:”在HTML中。
我猜现在这叫做混搭:-)
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.
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……
