问题
I'm writing a crawler to get content from some website, but the content can duplicated, I want to avoid that. So I need a function can return the same percent between two text to detect two content maybe duplicated Example:
- Text 1:"I'm writing a crawler to"
- Text 2:"I'm writing a some text crawler to get"
The compare function will return text 2 as the same text 1 by 5/8%(with 5 is words number of text 2 same text 1(compare by word order), and 8 is total words of text 2). If remove the "some text" then text 2 as the same text 1(I need detect the situation).How can I do that?
回答1:
You are facing a problem which is known in the field of Information Retrieval as Near Duplicates Detection.
One of the known solutions to it is to use Jaccard-Similarity for getting the difference between two documents.
Jaccard Similarity is basically - get sets of words from each document, let these sets be s1 and s2 - and the jaccard similarity is |s1 [intersection] s2|/|s1 [union] s2|.
Usually when facing near duplicates - the order of words has some importance however. In order to deal with it - when generating the sets s1 and s2 - you actually generate sets of k-shinglings, instead sets of only words.
In your example, with k=2, the sets will be:
s1 = { I'm write, write a, a crawler, crawler to }
s2 = { I'm write, write a, a some, some text, text crawler, crawler to, to get }
s1 [union] s2 = { I'm write, write a, a crawler, crawler to, a some, some text, text crawler, to get }
s1 [intersection] s2 = { I'm write, write a, crawler to }
In the above, the jaccard-similarity will be 3/8. If you use single words with the same approach, (k=1 shinglings) you will get your desired 5/8 - but this is worse solution in my (and most IR experts) opinion.
This procedure can be scaled nicely to deal very efficiently with huge collections, without checking all pairs and creating huge numbers of sets. More details could be found in these lecture notes (I gave this lecture few months ago, based on the author's notes).
回答2:
A good algorithm for comparing two text is tf-idf. It will give similarity between two documents.
1. calculate tf-idf for the document
2. calculate cosine similarity for two given text
3. the cosine similarity will indicate match between two documents.
This is a very good tutorial for calculating tf-idf and cosine similarity in Java. It would be simple to extend it to C#.
回答3:
In bioinformatics there is a algorithm which should do the job. It is called Needleman-Wunsch and is normally used for global sequence alignment with nucletide sequences.
Using this algorithm you could easily calculate accordance between two strings. You can use my code. But this method only returns the alignment you would have to calculate the accordance yourself.
来源：https://stackoverflow.com/questions/23053688/how-to-detect-duplicates-among-text-documents-and-return-the-duplicates-similar