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POTagging similar sentences with lower time complexity than n^2
 

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    <p>This is my first post, have been a lurker for a long time, so will try my best to explain myself here.</p> <p>I have been using lowest common substring method along with basic word match and substring match(regexp) for clustering similar stories on the net. But the problem is its time complexity is n^2 (I compare each title to all the others). I've done very basic optimizations like storing and skipping all the matched titles.</p> <p>What I want is some kind of preprocessing of the chunk of text so that for each iteration i reduce number of posts to match to. Any further optimizations are also welcome.</p> <p>Here are the functions i use for the same. the main function which calls them first calls word_match, if more than 70% of the word matches i further go down and call 'substring_match' and LCSubstr_len. The code is in Python, I can use C as well</p> <pre><code>import re def substring_match(a,b): try: c = re.match(a,b) return c if c else True if re.match(b,a) else False except: return False def LCSubstr_len(S, T): m = len(S); n = len(T) L = [[0] * (n+1) for i in xrange(m+1)] lcs = 0 for i in xrange(m): for j in xrange(n): if S[i] == T[j]: L[i+1][j+1] = L[i][j] + 1 lcs = max(lcs, L[i+1][j+1]) else: L[i+1][j+1] = max(L[i+1][j], L[i][j+1]) return lcs/((float(m+n)/2)) def word_match(str1,str2): matched = 0 try: str1,str2 = str(str1),str(str2) assert isinstance(str1,str) except: return 0.0 words1 = str1.split(None) words2 = str2.split(None) for i in words1: for j in words2: if i.strip() ==j.strip(): matched +=1 len1 = len(words1) len2 = len(words2) perc_match = float(matched)/float((len1+len2)/2) return perc_match </code></pre>
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