搭配词抽取

参考： 《Foundations of Statistical Natural Language Processing》第5章

实现了两种方法：互信息和卡方测试，直观上看卡方测试要好很多

#! /usr/bin/env python
#encoding: utf-8
#
# Extract collacation
#
#

import sys
import os
import math
from collections import defaultdict

g_word_map = {}
g_word_list = []

g_core_set = set()
g_exclusion_set = set()

def load_words(infile):
    word_set = set()
    if os.path.isfile(infile):
        with open(infile) as f:
            for line in f:
                word = line.strip().split('\t')[0]
                word_set.add(word)
    return word_set

def get_word_index(word):
    index = -1
    if word not in g_word_map:
        g_word_list.append(word)
        index = len(g_word_list) - 1
        g_word_map[word] = index
    else:
        index = g_word_map[word]
    return index

def calc_mi(word_freq, bigram_freq, doc_num, least_score, least_heat):
    '''
    calculate mutual information of two words
    '''
    for k, freq in bigram_freq.iteritems():
        if freq < least_heat:
            continue
        (i,j) = k
        n1 = word_freq[i]
        n2 = word_freq[j]
        mi = math.log(float(freq*doc_num)/n1/n2, 2)
        if mi < least_score:
            continue
        w1 = g_word_list[i]
        w2 = g_word_list[j]
        print "%s %s\t%.5f\t%d\t%d\t%d" % (w1, w2, mi, freq, n1, n2)

def calc_chi_square(word_freq, bigram_freq, doc_num, least_score, least_heat):
    '''
    calculate chi square score of two words
    '''
    for k, freq in bigram_freq.iteritems():
        if freq < least_heat:
            continue
        (i,j) = k
        w1 = g_word_list[i]
        w2 = g_word_list[j]
        n1 = word_freq[i]
        n2 = word_freq[j]
        e11 = freq
        e12 = n1 - freq
        e21 = n2 - freq
        e22 = doc_num - n1 -n2 + freq
        #print n1,n2,e11,e12,e21,e22
        valid = 0
        if e11 > 5 and e12 > 5 and e21 > 5 and e22 > 5:
            valid = 1
        denominator = n1 * n2 * (e22+e21)*(e22+e12) # when 28 24 0 0, denominator = 0
        chi = 1000
        if denominator > 0:
            chi = doc_num*(e11*e22-e12*e21)**2 / float(denominator)
        if chi < least_score:
            continue
        print "%s %s\t%.5f\t%d\t%d\t%d\t%d" % (w1, w2, chi, freq, n1, n2, valid)

def process_file(infile, seg_field, stop_file, method_type):
    '''
    extract collocation
    '''
    stop_set = load_words(stop_file)
    bad_pos = set(["c", "t", "r", "ad", "d", "f", "w", "o", "y", "p", "u", "q", "m"])

    doc_num = 0
    word_freq = defaultdict(int)
    bigram_freq = defaultdict(int)

    for line in file(infile):
        doc_num += 1
        s = line.rstrip('\n').split('\t')
        if len(s) <= seg_field:
            continue
        word_str = s[seg_field].lower()
        words = word_str.split("||") #format: hello@nx||world@nx
        word_set = set()
        for x in words:
            i = x.rfind('@')
            if i == -1:
                continue
            word = x[:i]
            pos = x[i+1:]
            #filter word
            if len(word) < 4 and pos != "a":
                if word not in g_core_set:
                    continue
            if pos in bad_pos:
                continue
            if word.find("{") != -1:
                continue
            if pos == "nx":
                if not word.isalpha(): #word.isalnum()
                    continue
            if word in stop_set:
                continue
            index = get_word_index(word)
            if index in word_set: #uniq
                continue
            word_freq[index] += 1
            word_set.add(index)

        # get bigram
        n = len(word_set)
        if n < 2:
            continue
        word_list = sorted(word_set) #note this! python set is a hashtable
        hit = 0
        hit2 = 0
        for i in range(n-1):
            for j in range(i+1, n):
                key = (word_list[i], word_list[j])
                # filter bad collocation
                w1 = g_word_list[key[0]]
                w2 = g_word_list[key[1]]
                if w1 in g_exclusion_set and w2 in g_exclusion_set:
                    continue
                bigram_freq[key] += 1

    #for k,v in word_freq.items():
    #    print "%s\t%d" % (g_word_list[k], v)
    #return

    if method_type == 'chi_square':
        least_score = 3.841
        least_heat = 5
        calc_chi_square(word_freq, bigram_freq, doc_num, least_score, least_heat)
    else:
        least_score = 1
        least_heat = 5
        calc_mi(word_freq, bigram_freq, doc_num, least_score, least_heat)

if __name__ == "__main__":
    if len(sys.argv) != 4:
        print "usage: %s <infile> <seg_field> <stop_file>"
        sys.exit(-1)

    g_core_set = load_words("hero.1") #can replace by the other file
    g_exclusion_set = load_words("hero.1")

    method_type = 'chi_square' # otherwise use mutual information
    process_file(sys.argv[1], int(sys.argv[2]), sys.argv[3], method_type)