blocks|key|416971|text|您可能需要查找Levenshtein+Distance实现。它显示了需要多少个字符插入/删除和替换才能使两个字符串相等。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|416972|这里有一篇关于C#库的文章，这些库实现了Levenshtein距离和其他文本比较算法：.NET+library+for+text+algorithms?。|416973|然而，我认为你必须使用一些方法的组合，因为使用Levenshtein会告诉你'Good+Company+Ltd.‘.’更像是“坏公司”。而不是“GoodCompany”。|416974|也许你需要通过扩展'str.‘来做一些预处理。移至“街道”并删除“中尉”就字符串比较而言，它是“无意义”的词。|416975|更新1|style|BOLD|416976|Francesco+De+Lisi+suggests使用拼音算法。看起来他们更适合比较拼写错误的名字。尽管如此，你仍然需要将地址分成语音/非语音部分(比如建筑编号)，然后分别进行比较。|416977|更新2|416978|至于地址比较，这篇文章是suggests+to+use+Google+Maps+API，另一篇文章讨论了address+parsing。我猜Google可以产生可靠的结果，因为他们有一个街道地址的数据库，在那里他们可以找到最正确的街道名称拼写。如果没有正确的街道/公司名称列表，您可能会遇到一些不正确的奇怪名称，但是许多不同的正确名称可能与其相似。|416979|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Levenshtein_distance|1|https://stackoverflow.com/questions/4508307/net-library-for-text-algorithms|2|https://stackoverflow.com/a/16713834/694852|3|https://stackoverflow.com/questions/518210/where-is-a-good-address-parser|4|https://stackoverflow.com/questions/16413/parse-usable-street-address-city-state-zip-from-a-string^0|7|K|0|0|17|X|1|0|0|0|0|3|0|I|8|2|0|0|3|0|C|V|3|1G|F|4|0^^$0|@$1|2|3|4|5|6|7|19|8|@]|9|@$A|1A|B|1B|1|1C]]|C|$]]|$1|D|3|E|5|6|7|1D|8|@]|9|@$A|1E|B|1F|1|1G]]|C|$]]|$1|F|3|G|5|6|7|1H|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|1I|8|@]|9|@]|C|$]]|$1|J|3|K|5|6|7|1J|8|@$A|1K|B|1L|L|M]]|9|@]|C|$]]|$1|N|3|O|5|6|7|1M|8|@]|9|@$A|1N|B|1O|1|1P]]|C|$]]|$1|P|3|Q|5|6|7|1Q|8|@$A|1R|B|1S|L|M]]|9|@]|C|$]]|$1|R|3|S|5|6|7|1T|8|@]|9|@$A|1U|B|1V|1|1W]|$A|1X|B|1Y|1|1Z]]|C|$]]|$1|T|3|-4|5|6|7|20|8|@]|9|@]|C|$]]]|U|$V|$5|W|X|Y|C|$Z|10]]|11|$5|W|X|Y|C|$Z|12]]|13|$5|W|X|Y|C|$Z|14]]|15|$5|W|X|Y|C|$Z|16]]|17|$5|W|X|Y|C|$Z|18]]]]

You might want to look for <a href="http://en.wikipedia.org/wiki/Levenshtein_distance" rel="nofollow noreferrer">Levenshtein Distance</a> implementation. It shows how many characters insertions/deletions and substitutions are required to make two strings equal.

Here is a post about libraries in C# that implement Levenshtein Distance and other text-comparison algorithms: <a href="https://stackoverflow.com/questions/4508307/net-library-for-text-algorithms">.NET library for text algorithms?</a>.

However I think you'll have to use some combination of methods, because using Levenshtein will tell you that 'Good Company Ltd.' is more similar to 'Bad Company Ltd.' than to 'GoodCompany'.

Maybe you'll have to do some preprocessing by expanding 'str.' to 'street' and removing 'Ltd.' as 'meaningless' word in terms of string comparison.

UPDATE 1

Francesco De Lisi <a href="https://stackoverflow.com/a/16713834/694852">suggests</a> to use Phonetic algoritms. Looks like they are better suited for comparing misspelled names. Still you'll need to split addresses to phonetic / non-phonetic parts (like building numbers) and compare them separately.

UPDATE 2

As for addresses comparison, this post <a href="https://stackoverflow.com/questions/518210/where-is-a-good-address-parser">suggests to use Google Maps API</a> for this purpose and another post discusses <a href="https://stackoverflow.com/questions/16413/parse-usable-street-address-city-state-zip-from-a-string">address parsing</a>. I guess that Google can produce reliable results because they have a database of street addresses where they can find the most correct street name spelling. Without list of correct street/company names you may encounter some strange name that is incorrect, however many different correct names would be similar to it.

blocks|key|1557643|text|你要找的是一个Levenshtein+distance+(Wikipedia)：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1557644|1557645|+...the+Levenshtein+distance是用于度量两个序列之间差异的字符串度量。非正式地说，两个单词之间的|blockquote|1557646|
距离是将一个单词转换为另一个Levenshtein所需的最小单字符编辑次数(插入、删除、替换|1557647|entityMap|0|LINK|mutability|MUTABLE|url|http://www.dotnetperls.com/levenshtein|1|http://en.wikipedia.org/wiki/Levenshtein_distance^0|7|K|0|T|9|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]|$A|X|B|Y|1|Z]]|C|$]]|$1|D|3|-4|5|6|7|10|8|@]|9|@]|C|$]]|$1|E|3|F|5|G|7|11|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|12|8|@]|9|@]|C|$]]|$1|J|3|-4|5|6|7|13|8|@]|9|@]|C|$]]]|K|$L|$5|M|N|O|C|$P|Q]]|R|$5|M|N|O|C|$P|S]]]]

What you're looking for is a <a href="http://www.dotnetperls.com/levenshtein" rel="noreferrer">Levenshtein distance</a> (<a href="http://en.wikipedia.org/wiki/Levenshtein_distance" rel="noreferrer">Wikipedia</a>):

<blockquote>
 ...the Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertion, deletion, substitution) required to change one word into the other
</blockquote>

blocks|key|1468364|text|Levenshtein在这种情况下是不合适的。"Good+Company+Ltd“和"GoodCompany”的距离为3，而"Good+Company+Ltd“和"Food+Ltd”的距离为1，但它们的含义完全不同。我建议使用Metaphone+or+Double+Metaphone算法。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1468365|使用online+metaphone+comparer的结果是：|1468366|1468367|+Good+Company+Ltd+=+KTKMPNLTT+GoodCompany+=+KTKMPN食品有限公司=+FTKMPNLTT+GoodCompanyLLC+=+KTKMPNLK|blockquote|1468368|1468369|1468370|这样你就知道GoodCompany，Good+Company+Ltd和GoodCompanyLLC是相似的，而Food+Company拼写错误或者完全不相关(KTKMPN包含在KTKMPNLTT和KTKMPNLK中，但不包含在FTKMPNLTT中)。|style|CODE|1468371|在here上查看其他算法的比较。|1468372|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Metaphone|1|http://www.searchforancestors.com/utility/metaphone.php|2|http://ntz-develop.blogspot.it/2011/03/phonetic-algorithms.html^0|35|T|0|0|2|P|1|0|0|0|0|0|6|B|I|G|Z|E|1J|C|27|6|2G|9|2Q|8|35|9|0|1|4|2|0^^$0|@$1|2|3|4|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]]|C|$]]|$1|D|3|E|5|6|7|17|8|@]|9|@$A|18|B|19|1|1A]]|C|$]]|$1|F|3|-4|5|6|7|1B|8|@]|9|@]|C|$]]|$1|G|3|H|5|I|7|1C|8|@]|9|@]|C|$]]|$1|J|3|-4|5|6|7|1D|8|@]|9|@]|C|$]]|$1|K|3|-4|5|6|7|1E|8|@]|9|@]|C|$]]|$1|L|3|M|5|6|7|1F|8|@$A|1G|B|1H|N|O]|$A|1I|B|1J|N|O]|$A|1K|B|1L|N|O]|$A|1M|B|1N|N|O]|$A|1O|B|1P|N|O]|$A|1Q|B|1R|N|O]|$A|1S|B|1T|N|O]|$A|1U|B|1V|N|O]]|9|@]|C|$]]|$1|P|3|Q|5|6|7|1W|8|@]|9|@$A|1X|B|1Y|1|1Z]]|C|$]]|$1|R|3|-4|5|6|7|20|8|@]|9|@]|C|$]]]|S|$T|$5|U|V|W|C|$X|Y]]|Z|$5|U|V|W|C|$X|10]]|11|$5|U|V|W|C|$X|12]]]]

Levenshtein is not appropriate in this case. "Good Company Ltd" and "GoodCompany" if trimmed have a distance = 3 while "Good Company Ltd" and "Food Company Ltd" have a distance of 1, but totally a different meaning. I suggest <a href="http://en.wikipedia.org/wiki/Metaphone">Metaphone or Double Metaphone</a> algorithm. 

Using <a href="http://www.searchforancestors.com/utility/metaphone.php">online metaphone comparer</a> the results are:

<blockquote>
<pre><code>Good Company Ltd = KTKMPNLTT
GoodCompany = KTKMPN
Food Company Ltd = FTKMPNLTT
GoodCompanyLLC = KTKMPNLK
</code></pre>
</blockquote>

In this way you know that <code>GoodCompany</code>, <code>Good Company Ltd</code> and <code>GoodCompanyLLC</code> are similar, while <code>Food Company</code> is misspelled or totally not related (<code>KTKMPN</code> is contained both in <code>KTKMPNLTT</code> and <code>KTKMPNLK</code> but not in <code>FTKMPNLTT</code>).

Look <a href="http://ntz-develop.blogspot.it/2011/03/phonetic-algorithms.html">here</a> for other algorithms comparisons.

I am looking for a library/class that allows smart compare of two strings. At best it would give as a result percent of how two strings are alike. I am comparing company names, addresses that are recordered in different repositories, thus having many misspellings or inconsistencies in names. 

Sample strings to compare: 

<pre><code> "Good Company Ltd." vs. "GoodCompany" 
 "Baker Street 2" vs. "Baker Str. 2" 
</code></pre>

If I get a result in percentage of alikeness, than this can be an input for smart merge of such data.

Do you know any good libraries that would allow such smart string compare?

Smart string comparison

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我正在寻找一个库/类，可以对两个字符串进行智能比较。充其量，它会给出两个字符串相似程度的百分比结果。我正在比较公司名称，地址记录在不同的存储库中，因此在名称中有许多拼写错误或不一致。要比较的示例字符串： "Good Company Ltd." vs. "GoodCompany"  "Baker Street 2" vs...

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