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PONER naive algorithm
 

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    <p>I never really dealt with NLP but had an idea about NER which should NOT have worked and somehow DOES exceptionally well in one case. I do not understand why it works, why doesn't it work or weather it can be extended. </p> <p>The idea was to extract names of the main characters in a story through:</p> <ol> <li>Building a dictionary for each word</li> <li>Filling for each word a list with the words that appear right next to it in the text</li> <li>Finding for each word a word with the max correlation of lists (meaning that the words are used similarly in the text)</li> <li>Given that one name of a character in the story, the words that are used like it, should be as well (Bogus, that is what should not work but since I never dealt with NLP until this morning I started the day naive) </li> </ol> <p>I ran the overly simple code (attached below) on <a href="http://www.umich.edu/~umfandsf/other/ebooks/alice30.txt" rel="nofollow">Alice in Wonderland</a>, which for "Alice" returns:</p> <blockquote> <p>21 ['Mouse', 'Latitude', 'William', 'Rabbit', 'Dodo', 'Gryphon', 'Crab', 'Queen', 'Duchess', 'Footman', 'Panther', 'Caterpillar', 'Hearts', 'King', 'Bill', 'Pigeon', 'Cat', 'Hatter', 'Hare', 'Turtle', 'Dormouse']</p> </blockquote> <p>Though it filters for upper case words (and receives "Alice" as the word to cluster around), originally there are ~500 upper case words, and it's still pretty spot on as far as <a href="http://en.wikipedia.org/wiki/Alice%27s_Adventures_in_Wonderland#Characters" rel="nofollow">main characters</a> goes.</p> <p>It does not work that well with other characters and in other stories, though gives interesting results. </p> <p>Any idea if this idea is usable, extendable or why does it work at all in this story for "Alice" ?</p> <p>Thanks!</p> <pre><code>#English Name recognition import re import sys import random from string import upper def mimic_dict(filename): dict = {} f = open(filename) text = f.read() f.close() prev = "" words = text.split() for word in words: m = re.search("\w+",word) if m == None: continue word = m.group() if not prev in dict: dict[prev] = [word] else : dict[prev] = dict[prev] + [word] prev = word return dict def main(): if len(sys.argv) != 2: print 'usage: ./main.py file-to-read' sys.exit(1) dict = mimic_dict(sys.argv[1]) upper = [] for e in dict.keys(): if len(e) &gt; 1 and e[0].isupper(): upper.append(e) print len(upper),upper exclude = ["ME","Yes","English","Which","When","WOULD","ONE","THAT","That","Here","and","And","it","It","me"] exclude = [ x for x in exclude if dict.has_key(x)] for s in exclude : del dict[s] scores = {} for key1 in dict.keys(): max = 0 for key2 in dict.keys(): if key1 == key2 : continue a = dict[key1] k = dict[key2] diff = [] for ia in a: if ia in k and ia not in diff: diff.append( ia) if len(diff) &gt; max: max = len(diff) scores[key1]=(key2,max) dictscores = {} names = [] for e in scores.keys(): if scores[e][0]=="Alice" and e[0].isupper(): names.append(e) print len(names), names if __name__ == '__main__': main() </code></pre>
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