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    <p>From the examples you give, it sounds like you have more a natural language problem than an exact matching problem. Given that natural language matching is inexact by nature you're unlikely to come up with a perfect solution. </p> <ul> <li>String distance doesn't really work, as strings that are algorithmically close may not be semantically close (e.g. "DJ Above &amp; Beyond" should match "Above and Beyond" but not "DJ Above &amp; Beyond 2" which is closer in Levenshtein distance.</li> <li>Some cheap alternatives to natural language parsing are <a href="http://en.wikipedia.org/wiki/Soundex" rel="nofollow noreferrer">soundex</a>, which will match by phonetic sounds, and <a href="http://en.wikipedia.org/wiki/Stemming" rel="nofollow noreferrer">Stemming</a>, which removes prefixes/suffixes to normalize on word stems. I suppose you could create a linked list of word roots, but this wouldn't be terribly accurate either. </li> <li>If this is a User-interacting program, you could echo "near misses" to the user, e.g. "Is one of these what you meant to enter?"</li> <li>You could normalize the entries in some way so that different entries map to the same normalized value (e.g. case normalize, "&amp;" -> "And", etc, etc. which some of the above suggestions might be a step towards) to find near misses or map multiple inputs to a single value.</li> </ul> <p>Add the caveat that my experience only applies to English, e.g. an english PorterStemmer won't recognize the one French title you put in there.</p>
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    1. POMerging duplicates in a list? - Question is more complex than it seems
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    1. COThanks Steve, there is a lot of useful info in your answer. How would **stemming** work for **"Above and Beyond"** and **"DJ Above & Beyond"**? At first impression it sounds like a way of normalising "nicknames", I haven't mentally processed it's utility yet. :)
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    2. COStemming is a word-based, not a phrase-based process.The idea is to normalize grammatical forms to their word stem, e.g. Ran, Run, Running -> Run, basically by encoding suffix, prefix removal as well as the many special cases. Would take you forever except that it's been done, code examples are out there. I was thinking you could transform a phrase into a linked list of word stems. I'm sure there are cases where this would break too (e.g. "Dumb and Dumber"->"Dumb and Dumberer"), as what you're trying to do is by nature a fuzzy problem. Maybe in conjunction with other strategies...
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