对话系统评价指标Greedy Match代码实现

 

Greedy Matching  贪婪匹配方法是基于词级别的一种矩阵匹配方法，在给出的两个句子r和r^，每一个词w∈r都会经过词向量转换后变为词向量ew，同时与r^中的每一个词序列w^∈r^的词向量ew^最大程度进行余弦相似度匹配，最后得出的结果是所有词匹配之后的均值:

首先要去glove官网https://nlp.stanford.edu/projects/glove/下载训练好的英文词向量压缩包，我选择的是glove.840B.300d.zip，解压得到文件glove.840B.300d.txt，你可以下载比较小的包，解压并保存到你创建的项目下，中文的话要自己去腾讯的AILab下载中文词向量，以下是实现计算两个句子基于词级别的贪婪匹配代码：

import numpy as np
import re

def cosine_similarity(x, y, norm=False):
    """ 计算两个向量x和y的余弦相似度 """
    assert len(x) == len(y), "len(x) != len(y)"
    zero_list = [0] * len(x)
    if x == zero_list or y == zero_list:
        return float(1) if x == y else float(0)

    # method 1
    res = np.array([[x[i] * y[i], x[i] * x[i], y[i] * y[i]] for i in range(len(x))])
    cos = sum(res[:, 0]) / (np.sqrt(sum(res[:, 1])) * np.sqrt(sum(res[:, 2])))

    return 0.5 * cos + 0.5 if norm else cos  # 归一化到[0, 1]区间内


def conver_float(x):
    '''将词向量数据类型转换成可以计算的浮点类型'''
    float_str = x
    return [float(f) for f in float_str]

def process_wordembe(path):
    '''
    将词向量文件中的所有词向量存放到一个列表lines里
    :param path: a path of english word embbeding file 'glove.840B.300d.txt'
    :return: a list, element is a 301 dimension word embbeding, it's form like this
            ['- 0.12332 ... -0.34542\n', ', 0.23421 ... -0.456733\n', ..., 'you 0.34521 0.78905 ... -0.23123\n']
    '''
    f = open(path, 'r', encoding='utf-8')
    lines = f.readlines()
    return lines


def word2vec(x, lines):
    '''
    将一个字符串(这里指句子）中所有的词都向量化，并存放到一个列表里
    :param x: a sentence/sequence, type is string, for example 'hello, how are you ?'
    :return: a list, the form like [[word_vector1],...,[word_vectorn]], save per word embbeding of a sentence.
    '''
    x = x.split()[:-1]
    x_words = []
    for w in x:
        for line in lines:
            # print(line)
            if w == line.split()[0]:   # 将词向量按空格切分到一个列表里，将列表的第一个词与x的word比较
                print(w)
                x_words.append(conver_float(line[:-1].split()[1:]))  # 若在词向量列表中找到对应的词向量，添加到x_words列表里
                break
    return x_words


def greedy(x, x_words, y_words):
    '''
    上面提到的第一个公式
    :param x: a sentence, type is string.
    :param x_words: list[list1, list2,...,listn], listk(k=1...n) is word vector which from sentence x,
    :param y_words: list[list1, list2,..., listn], listk(k=1...n) is word vector which from sentence y,
    :return: a scalar, it's value is in [0, 1]
    '''
    cosine = []  # 存放一个句子的一个词与另一个句子的所有词的余弦相似度
    sum_x = 0   # 存放最后得到的结果
    for x_v in x_words:
        for y_v in y_words:
            cosine.append(cosine_similarity(x_v, y_v))
        if cosine:
            sum_x += max(cosine)
            cosine = []
    sum_x = sum_x / len(x.split()[:-1])
    return sum_x


def greedy_match(path, x, y):
    '''
    上面的第二个公式
    :param lines: english word embbeding list, like[['-','0.345',...,'0.3123'],...]
    :param x: a sentence, here is a candidate answer
    :param y: a sentence, here is reference answer
    :return: a scalar in [0,1]
    '''
    lines = process_wordembe(path)
    # x_words.append(line.split()[1:] for line in lines for w in x if w in line)
    x_words = word2vec(x, lines)
    y_words = word2vec(y, lines)

    # greedy match
    sum_x = greedy(x, x_words, y_words)
    sum_y = greedy(y, y_words, x_words)
    score = (sum_x+sum_y)/2
    return score


if __name__ == '__main__':
    # print(cosine_similarity([1, 1], [0, 0]))   # 0.0
    # print(cosine_similarity([1, 1], [-1, -1]))  # -1.0
    # print(cosine_similarity([1, 1], [2, 2]))  # 1.0
    f = open('G:\\PycharmProjects\\test\\glove.840B.300d.txt', 'r', encoding='utf-8') #这里改成你自己项目的路径
    path = 'G:\\PycharmProjects\\test\\glove.840B.300d.txt'  #这里改成你自己项目的路径

    # lines = process_wordembe(path)
    # print(lines[:1])

    x = "what 's wrong ? \n"
    y = "I 'm fine . \n"
    # x_words = word2vec(x, lines)
    # y_words = word2vec(y, lines)
    # print(x_words[0])
    # print(y_words[0])
    # sum = greedy(x, x_words, y_words)
    # print(sum)

    score = greedy_match(path, x, y)
    print(score)

 

如有问题，欢迎评论指正。